Theoretical Background of Digital Pathology

Information in Pathology

The term “information” has been derived from the Latin word informare which can be translated 'create by teaching’. It is a potential or existing pattern of material or energy, which can be recognized by a dynamic (living) system and influences its further behavior. Information includes two important features, namely (I) a source (pattern, texture, model, etc.) and (II) an “understanding receiver”. In practice, additional important parameters are the constancy of recognition and a new constellation for the living system. In other words, information forces the “receiver” to acting in a certain direction, and thus diminishes its uncertainty. The “measure” of information is usually performed by application of Claude Shannon’s mathematical theory of communication, which has been published in 1948 [^430,431]. This theory derives an “information measure” from the statistical distribution of individual signals. Frequent (or common) signals possess less information than rare events. For an individual biological system, the most significant required information is: How to stay alive? Within this general structure, at least three different sub-types of information are prerequisite: a) to detect the necessary energy sources (food), b) to avoid individual catastrophes (detect enimies), and c) to provide the continuation of the species (reduplicate). How does this principle reflect to a complex living system?

Biological Information

As stated above, information requires a “repeatable” distribution of structures or events, which have to be distinguishable from the “background”. The repetition of events is obviously needed for “learning”: To learn from an event, which does no longer exist in the future is of no use and a waste of energy or resources. The complexity of living organisms requires a spatially and temporally synchronized exchange of information at different levels. We are used to analyze these levels from the “microcosmos” to the “macrocosmos”, i.e., from the arrangement of macromolecules to synchronized organoid and cellular structures including electronic signals, followed by boundaries, that separate inner from outer spaces, and finally steered movements and behavior of a whole individual. At all these levels, information is acquired from external sources, internally stored, and finally released from two storage systems, a long-term stored “data bank” and a flexible dynamic data flow often in combination with suppressor functions.. At the level of molecular biology or genetics, the fixed (internal) information storage system is the (desoxy) ribonucleid-acid (DNA), at the complex level of psychiatry it is called memory. The dynamic data flows include exchange of ions and changes of three-dimensional structures at the level of molecular biology, and those of acoustic and visual information at the behavior level. Information restricts the “degrees of freedom” of, and, therefore, opens a “goal” for the biological system. The different “levels of information” assure the flexibility of the system, which is often realized in “feed back mechanisms”. From this point of view, measurements of biological information sources include the definition of “limits to estimate healthy persons” and aberrations to define the nature and extent of a disease. Characteristic examples are the serum sugar concentrations (diabetes mellitus), electrocardiograms (ECG arrhythmias, cardiac infarction, etc.), or DNA (flow) cytometric measurements to distinguish between benign and malignant diseases. The repetition of information structures is realized in curves (time repetition) and in organoid structures (space repetition). Naturally, time repetitions reflect to space repetitions too; however, often only at specific organoid “levels” (fiber contractions, ion channels, etc.). The correspondent “space related” examinations include mainly molecular biological techniques such as comparative genomic hybridization (CGH), genomic arrays, or syntactic structure analysis at a light microscopic level. The measurements of biological information require both a characterization of the information structure and its contribution to the future behavior of the system (restriction of freedom degrees). Of major interest is the “outcome” of the whole system, i.e., its general health condition. The smaller the involved structure the more interactions have to be taken into account. The interpretation of basic molecular pathology data need detailed knowledge of the clinical and individual aspects of the involved patient. From the mathematical point of view strategies how to handle small event-large parameter sets (bioinformatics), and how to compare different images are the challenge. Interestingly, the majority of mathematical procedures to be applied has been developed at the beginning and middle of the last century; however, only nowadays high-speed and well-organized computers can perform the necessary calculations. The molecular-biological information is created by space- and time related structures, commonly called morphology and function. What is their relationship?

Morphology and Function

Our environment seems to be embedded in a four dimensional space which is considered to act as a framework for all phenomena in nature. Physical laws regulate the correlation between the different independent dimensions, which can be subdivided into three congruent, non-oriented dimensions (space) and an oriented independent one (time). Functions are relations between time and space, and the functions can be time independent, i.e., only space related, for example field forces such as gravity, electromagnetic fields, etc., or-in addition-time-dependent, for example laws of irreversible thermodynamics. In order to “detect” a function, a certain “physical equivalent” must exist in at least one of the space-associated dimensions, for example a point, line, ring, ball, stone, plant, animal, etc. In living or other time-dependent systems these “arrangements” usually have a specific time-characteristic [^103,245,256]. After a period of a close time-relationship they seem to stay “stable” or nearly time-independent, followed by a period of strict time-relationship with a finally disarrangement or decay of the system. The outer and inner arrangement of these space-time equivalents, and their position and composition in biological or living systems at a certain time is called morphology. As it is considered to be an expression of genetic information (genotype) it is also called phenotype. It can be visualized by techniques that “fix” the spatial arrangement at a certain stage of development (fixation). Specific visualization techniques (staining procedures) allow the recognition of biological important structures such as membranes, nuclei, proteins, DNA fragments, etc, which reflect to the informative behavior of the whole system. These techniques opened a door into the world of cellular arrangement in organs with normal or healthy tissues, and its disturbance under conditions of abnormal functions. Until now, especially abnormal tissue growth could not be treated without knowledge of the cancer morphology. This “conventional light microscopic world” has been expanded to sub-cellular structures by use of electron microscopy and scanning electron microscopy, and to functional stages by visualization of the expression of macromolecules in certain stages of cellular development or abnormal growth, i.e., by immunohistochemical and ligandohistochemical staining techniques and related molecular-biological methods such as in situ hybridization or chromosome banding techniques. At a first glance, the knowledge of the presence or absence of a certain biological structure seems to be sufficient for disease classification and subsequent treatment of the patient; however, already a short reflection to the interactive information processes at the numerous information levels displays a functional complexity which is very unlikely to result in a simple binary (yes-no) reaction of the system.

What is the fundamental informative relationship between the expression of a certain biological structure and the associated function? What is the reason that abnormal cellular or organ function can be recognized by disarrangement of the associated geometrical “sources”? In other words, why have the cells given up their normal appearance once they have started to loose their normal function? It seems justified to assume that all biological processes or functions have to follow the general physical laws present in our environment. In addition, living system belong to so-called thermodynamically open systems. These systems are characterized by exchange of free energy and heat or entropy with their environment, and may stay on a low level of entropy for a long time by import of free energy and export of the produced entropy. The exchange of information is just a different word to characterize this energy import and heat export. A spatial repetitive import and export reflects to a (nearly) identical information exchange within the same informative level, for example an organ or its functional compartment. A normal function characteristic of an organ is that at least a group of neighboring cells is nearly identical in their energy and entropy balance. Thus, they posses the same level of entropy and current of entropy. Their exported products are identical structures (for example molecules, daughter cells, etc.) and are then arranged in a regular manner, which is basically defined by the energy forces and position of the “stem” cells. This is a prerequisite of a correct information transfer at a higher level. A disturbance of the cellular function has to be associated with a different energy balance that will lead to an “irregular distribution of derived structures”. Even when the “products” of the cells with different functions remain still identical, their final spatial arrangement will become altered, as the “steering” mechanism (feedback) of the higher order structures is diminished [^258,278,280]. The disarrangement increases, when, in addition, the products also differ. As a result, the regularity or symmetry of a tissue reflects the homogeneity of cellular function, and the status of this homogeneity can be derived from the structure analysis. Analyzing spots of functional disturbances such as cancer, the level of this disturbance can be estimated by the calculation of the total of energy or entropy, which has to be added to a normal structure to create an abnormal texture. The following equations can be derived using micro-state distances (structures) and the expression of normal field forces:

ES (MST) = -k * S{p_k *ln(p_k)};             (1)

p_k = 1/(N-1) * [Σ Σ ( Δ_mik/Δ_mik)²}]

k = Σ(m_mk/m_k)² = standardization constant

Δm_ik = microstate differences between neighboring events

           (distance, mass, staining intensities, etc.)

mm_k, Δm_k = mean of microstates in the macrostate k.

Measuring only the cellular proliferation will result in:

Σ Σ{ ( Δ_mik/m_mik)²} = Σ[(Δm/m)² + (Δd/d)²]

        and       CE = d(ES)/dt * x(1/s)              (2)

______________________

ES = structural entropy

Dm = difference of DNA content between neighboring cells

m = mean DNA content of cells under consideration

Dd = difference of distance between neighboring cells and mean cellular distance

d = mean cellular distance

CE = current of entropy

s = surface of the system under consideration (tumor)

It could be shown that the structural entropy and current of entropy are good estimators for the survival of patients with bronchus carcinoma. The details are given in [^{234,245,256,272,282,285}].

Additional texture-associated features such as mean distance between neighboring cells, distance between neighboring cells of different cell types, for example tumor cells and lymphocytes, have been reported to posses biological significance in various types of lung cancer. Basically, the terms Dm and m include features of vertices that originate an informative structure (graph) at a certain information level, and the calculated structural entropy is a measure of information disturbance within the system.

In practice, the visual analysis of the arrangement (attributed graphs) of biological “units” is a close derivative of the deviation of normal function and information transfer. The analysis of tissue structures is, therefore, an important task in the diagnosis and treatment of human diseases, especially in cancer patients. These measurements can be routinely performed on a broad variety of features, such as nuclei (DNA content), proteins (immunohistochemistry), DNA sequences (FISH), or vessels. How do the different biological bricks interact or fit together in terms of information storage and release?

Genotype and Phenotype

It is generally accepted, that the main level of biological information reflects to the individual cells of an organism, and that the basic information at this level is stored in the DNA located in cellular nucleus. Dependent upon the needs of the cell, this information can be retrieved and transferred into action by specific DNA-RNA pathways. The final outcome is a set of proteins that create new structures by destroying existing ones or interactions. The structural appearance of a biological system is, therefore, closely associated with the information stored at the DNA level. This storage is distinct, and can be subdivided into individual units, called genes. The genotype describes presence, absence and structural arrangement of genes within the DNA molecule, the phenotype the morphology at the level of cellular arrangements. Although the phenotype is a derivative from the genotype, the numerous interactions in releasing genetic information and creating new structures prohibit an individual genotype-phenotype expression. To give an example, cancer is nowadays considered to be a disease of “gene-abnormalities”, and, in fact, numerous cancer types have been reported to be associated with genetic disturbances. These include over-expression of oncogenes or alterations of so-called suppressor genes. An increased risk of breast carcinomas has been observed in patients with a BRCA-1, BRCA-2 or BRCA-3 gene. The percentage of breast cancer in women carrying this gene has first been calculated to about 90%, other reports give a considerably lower risk of 70%-80% [^{69,101,133,150,156,186,191,220,298,373,416,477,516}]. Other genes such as retinoblastoma gene or polyposis coli gene contribute to similar percentages to the corresponding cancer types [^{66,79,134,166,225,227,406,413,459,472,515,524}]. The existence of specific abnormalities at the genetic levels does not necessarily imply the outbreak of a disease that is obviously close associated with genetic information. The diagnosis of cancer still remains a domain of phenotype and of two dimensional image analyses at light microscopic or cellular level. Independent from the influence of external factors, of genotype or phenotype, there is no doubt that any disease is associated with a disturbance of information exchange at various biological levels. Thus, the question arises: What kind of information is presented in a histological image? How can it be recognized and implemented into a tissue-based diagnosis?

Information Analysis of Histological Slides

The information content of light microscopy images obtained from conventionally stained glass slides is composed of two main compartments, namely a) object-associated information, and b) non-object associated information. The different information components that contribute to tissue-based diagnosis are shown in (table 1).

The detection and classification of object-associated information requires a “division” of the image into an object-related space (compartments), and a non-object-related space (background) [^{263,307,388,276,193}]. The objects searched for are usually “abnormal” events (cancer cell nuclei, inflammatory cells, external material, etc.), i.e. objects which display unusual features or which are not present in the analyzed tissue under normal (healthy) circumstances. For example, they comprise cells with alterations in size or internal structures (virus infection), cancer cells, inflammatory cells, or external organisms (bacteria, parasites). The majority of “classic” diagnoses is based upon the detection and correct identification of these “objects”: A correct cancer diagnosis requires the correct and error-free proof of cancer cells, that of active tuberculosis the visualization of tuberculosis bacilli. The basic scheme of object-related diagnosis procedures is given in (figure 1). The first step is to divide the original image into an object and a background image. The second step analyses the objects in relation to their features (cellular and nuclear size, staining intensity, form factor, etc). Of major significance is the procedure of the object-background separation (thresholding), which can be object dependent or not [³⁸].

Having identified the objects, their spatial arrangement can possess diagnostic information too, for example in specific growth pattern (granulomas, adenoid growth pattern, epidermoid cellular arrangements, etc.). These features can be analyzed by various techniques, for example by syntactic structure analysis [^{34,35, 231,234,263,284,310,314}]. A related graph is constructed which represents the gravity centers of the objects (nodes), a neighborhood relationship (edges), and node/edge related attributes (distances, sizes, integrated optical density, etc.). The procedure allows the definition of new (higher order) objects, if statistical associations (or repeated geometrical figures) can be obtained [^{230,245,284,480}]. The spatial arrangement of identified objects is called structure. The location of objects can be randomly distributed or might follow certain rules. These rules are common in healthy tissues and include cellular agglutinations, strict neighborhood conditions between different cell types or formation of regular patterns.

Fig. 1: Characteristics of object-related diagnosis algorithm in tissue-based diagnosis

In addition to the described procedures, non-object oriented information can be extracted from a histological image. The underlying representation of image information is usually called texture, and the procedure texture analysis [^{193,212,259, 314,457}]. A texture is a gray value distribution, which might possess invariants in image transformation (symmetries). A texture can be analyzed by an autoregressive procedure that computes the gray values of pixels in relation to those of their neighbors. Similar, the same procedure can be applied to create images with artificial textures. A reproducible and invertible texture analysis results in a set of 5–6 parameters, and is, therefore, an appropriate tool to compute “similarities” between different images. It can also be used to transform an image into a two dimensional matrix and to compare images with known textures to those under diagnostic examination [¹⁹³]. An example of the technique is given in (figure 2).

Fig. 2: Example of textures derived from autoregressive and local gray value (thinning) algorithms

The application of both object and texture associated diagnosis procedures results in a data set that represents the image as a whole. Thus, these algorithms seem to be useful to analyze virtual slides, which represent complete digitized glass slides. However, the digitalization of a complete glass slide creates images measuring several GB in size [^{263,284 107,122,123,534}]. Therefore, the question arises whether the diagnosis information content of the complete image can be extracted from included image compartments, and, if yes, what is the obtained accuracy, i.e. sensitivity and specificity?

Object oriented Image Information

Obviously, image information is addressed to a viewer (or observer), who will specifically react if she or he receives (can see) the displayed information. In principle, image information is related to certain area (or space) associated features that “can be recognized”. The displayed features permit to separate these areas from the other compartments of the image. The total of both compartments add up to the complete image without overlapping. The distinct “information possessing areas” are called “objects”, and the remaining part “background”. The features of an object define its appearance, and can be computed in gray values of distinct areas (pixels) that lie within and outside of the object. Naturally, distinct areas that contain different quantities and quality of information might be present. The algorithm to detect the image objects is, therefore, a prerequisite to seeing specific image information, and to react appropriately, for example, to state a diagnosis. The object detection method is called segmentation. The accuracy of object segmentation depends upon

1. the size of the objects in relation to the total image size (area or volume fraction)
2. the gray value differences of pixels that are located at the object boundary from those that are located in the surrounding neighboring image areas (gradient).
3. the possibility to add external information to the segmentation procedure, and
4. the applied segmentation procedure.

Naturally, the size of an object has to fit into the whole image to be correctly identified. In tissue-based diagnosis, the location of objects within the image

frame is given by random, i.e. cannot be predefined. Thus, the size of the field-of-view has to measure a multiple of that of the object to be identified. On the other hand, if the object size is too small in comparison to that of the view field, the object cannot be identified accurately, for example, by its size or internal gray value distribution. The reasons are the biological variation in size and features, and the decrease of gray value differences at the object boundary. Using a conventional microscope, the common magnification to identify objects in classic diagnosis ranges from 200:1 to 800:1.

The gray value differences between pixels at the object boundary and those of the surrounding “outside” play the main role in detecting and separating any “information area” from the background. The greater and the more homogeneous the difference the easier is the segmentation. Variations that occur due to biological variance or image noise can be smoothed down or bridged using adequate filters; however, only to a certain degree. In conventional tissue-based diagnosis, the segmentation is either performed in a certain color space (nuclei usually stain blue) or in a singular gray value space that is artificially created from all three color spaces by specifically chosen color transformation. A commonly used method is the so-called principle component analysis and applying the Karhune-Loewe expansion [³⁰⁹, ^{377 92}]. To use, in addition, external image information sources to correctly identify an object is still under development in tissue-based diagnosis, and has not been broadly come into practice to our knowledge. The technological approaches focus on the application at the stage of image acquisition, and the so-called smart cameras have to be mentioned here [^{73 486 464}].

The implementation of smart cameras does not rely on a centralized server, and might use peer-to-peer computing to perform the various steps in the gestalt recognition system across the network: so-called distributed smart cameras [⁸⁹] The underlying principle describes a set of object features that are no longer related to the object as a whole. In contrast, the object itself is considered to act as the “whole image”, and internal compartments are selected that are characteristic of the complete object to a high degree. A characteristic example is shown in (figure 3). A shark can be recognized by its fin already if it is seen above the sea surface although the entire body of the animal is hidden in the water. The shape and size of the fin are characteristic for this animal. Thus, the general shape of the shark and its location can be derived solely from its fin. In tissue-based diagnosis, the only similar algorithms have been applied to “characterize” the image background in relation to detected objects. This example analyzes the number of proliferating tumor cells in association to their distance from the nearest neighboring vessel ²²⁸. Additional applications, for example detection of tumor cells in small biopsies, or artificial display of most likely tumor growth in areas outside the field-of-view have not been reported to our knowledge until now.

Fig. 3: Example of external information-related feature extraction. The shark can be identified by knowing the internal association between its different features (fin, body size, water)

Conversely, several scientists have investigated the most appropriate segmentation procedures [^{47,155,281,337,350,463}]. Accurate object segmentation is the prerequisite for detection of any object-associated information. The segmentation procedures can be applied with or without prior image transformations (for example, eroding and dilating). They either use a fixed segmentation threshold or a dynamic one with a fixed gray value difference at the object boundary. The dynamic procedure can be applied to the whole range of gray values or only within predefined limits. Whether one of the described procedures is superior to the others, remains an open question. It probably depends on the image quality and the “nature” (appearance) of the objects to be identified. In tissue-based diagnosis, especially in identification of immunohistochemically stained objects the dynamic segmentation methods seem to be more appropriate since they take into consideration unavoidable variations in color and intensity [^231,263,267].

The segmentation of objects is followed by the necessary next step of object identification, as not all non-background events belong to an information-related object class. The object-associated diagnosis includes all classes of diagnosis, which describe a momentary situation of the disease, for example adenocarcinoma, tuberculosis, angiitis, etc. They also include terms of molecular biology and molecular genetics, such as neuroendocrine positive tumors, or expression of the HER2-neu gene. Speed- and severity-associated features are usually not derived exclusively from object-based properties. They require additional analyses of spatial relationship between one or several sets of objects, and are, thus, texture-associated.

The efficient identification of diagnosis-relevant objects requires a “balance” between the necessity to identify an object without diagnostic errors, and the probability to “find” the corresponding objects within the field-of-view. What is a reliable solution, and how does it work?

Application of Nyquist’s Theorem on Object oriented Information

The image obtained from digitalization of a complete glass slide is called “virtual slide”. A virtual slide image file may be several GB in size [^{86,107,112,182,314,344,407}]. It is generated by the acquisition of multiple image compartments that are “patched together” (patchwork procedure) [^{263,311,312,344,497,503}]. The image acquisition time for a virtual slide ranges from minutes to hours, dependent, in part, on the desired image resolution. Once such a virtual slide image file has been acquired, it can be used for numerous purposes including image quantification, storage and retrieval in routine diagnostic work, steering source for automated tissue sampling in tissue micro arrays (TMA), continuous education, etc. The handling of such a very large data matrices, however, is not easy, and requires fast communicative connections and sophisticated programming. In addition to fast line connections and smart computer solutions, appropriate use of sampling procedures might be useful, might save time and avoid non-necessary efforts. One idea is based upon the principle of tissue-based diagnosis: Once the necessary information needed for diagnosis statement (and confirmation) has been detected, all additional investigations are stopped, i.e., the diagnostic procedure will be terminated immediately. For example, if tumor cells can be clearly identified in one or several image compartments, there may be no need to further analyze additional areas, (or the whole image), when further analysis will not affect the diagnosis.

The decomposition of an image into “diagnosis compartments” and their analysis might, therefore, improve the efficiency of a diagnostic procedure and further allow the calculation of the “risk” of missing an object with diagnostic significance. The risk calculation of object-associated diagnosis depends upon the object number and their size with relation to the sizes of the chosen compartments, as well as upon their sizes and number in respect to the size of the original image. If we consider the probability of an object diagnosis as “original diagnosis frequency” and the compartment division of the original image as “digitalization”, we can apply Nyquist’s theorem for an optimal adjustment of compartment size to the size of the complete image. According to Nyquist’s theorem the signal to be reconstructed must be sampled with a frequency at least two times greater than that to be reconstructed [²⁶³]. In other words, the number of pixels required to classify an object should amount to two (in a two dimensional space four) times more than the lower limit of recognition. Similar, the size of the “sampling space”, i.e. the diagnosis image compartment, must amount four times more than the pixel size of the objects divided by the relative frequency of objects present in the complete image. This assumption is useful for analysis of histological images, as these images usually contain connected tissue compartments.

A simple computation assuming that 10% of the original image (virtual slide) contain diagnostic significant objects, the size of an object measures 100 mm², and an objective of *20 is required to identify the object results in a sample size 400 mm², which should be repeated N = 1,.2. …10 times using randomly selected non-overlapping samples. If one of the samples contains an object, the procedure can be terminated. An overview of sample size useful for frequently diagnosed histological objects is given in (table 2).

A Survey of Sampling Procedures

The object-oriented information-extraction requires the identification of objects. The necessary algorithms can be applied to a histological image a) with or b) without additional spatial-associated predefined knowledge. This statement reflects to a random or non-random selection (sampling) of image compartments to be analyzed [^231,263,267]. Basically, at least five different sampling procedures can be distinguished in the analysis of histological slides. They reflect to a) the aim of the image analysis, for example to evaluate the diagnosis information with the highest efficiency, b) to biological features or expected object properties, for example environment independent exhibition of receptors (visualization of macromolecules).

Random sampling does not require predefined information input, and is usually applied for measuring object properties, and the spatial distribution of objects within the tissue. Its counterpart is called stratified sampling, a procedure which either stops when identifying a wanted object (cancer cell), or preferable takes place in certain image areas (for example in close neighborhood to a vessel, at image compartments that display certain specific features, etc.). Both methods, i.e., random sampling and stratified sampling are object-oriented. They can be performed with a local independent (passive sampling) or local dependent (active sampling) object identification strategy. Active sampling procedures are often necessary in images that visualize macromolecule expression due to image features that are induced by laboratory conditions.

Finally, quite often “unknown” objects are identified that are difficult to be distinguished from artifacts. They are rare in frequency; might, however, belong to common objects, which express uncommon features (artifacts). The correct classification of these objects requires an event-and space-related identification of known surrounding objects, and is called functional sampling [^231,267]. The application of stratified either active or passive sampling is most promising for automated extraction of diagnosis-oriented information from a histological image.

Texture Oriented Image Information

In contrast to object-related information, textures can be derived without the division of an image into a foreground (object space) and into a background. Unfortunately, an exact definition of a texture does not exist to our knowledge, neither in general nor in the context of image analysis. Most of the authors use the term “texture” for a general gray value function which can be derived from several repeating and “easy to see” basic image patterns. For example, according to Tamura et al. (1978) a texture can be defined by coarseness, contrast, directionality, line-likeness, regularity, and roughness [⁴⁵⁷]. Another, more practical and promising approach has been proposed by Voss and Süße [⁴⁸⁰]. The authors use an auto-regression function derived from the analysis of time sequences to describing and to creating textures. A six dimensional stochastic differential equation describes the correlation of random values (gray values) which are modified by associated coefficients. The basic algorithm is demonstrated in (figure 4). An example showing the original image, best fitting randomly computed objects and the calculated texture of a histological image (figure 5).

Fig. 4: Matrix presentation of the autoregressive texture analysis algorithm

Fig. 5: Microphotograph of a squamous cell carcinoma of the lung (H & E, x20), its derived texture and best-fitting artificial texture and objects. The identification of objects and the analysis of textures are the two columns of information extraction from a histological image. Man is doing this by appropriate training and gathered experience. How can automatically a machine act in this manner? This machine obviously requires a certain amount of “intelligence”, commonly called artificial intelligence 

The algorithm is basically dependent upon the image size; it becomes, however, quite independent for images of > 2,500 pixels in size (50 * 50 pixels) [²⁶³].

The texture synthesis using this auto-regression model and the corresponding derivation of textures from an image to be diagnosed permits a comparison of textures, and the computation of texture similarities. This idea seems be appropriate to determine useful diagnostic information based upon image textures.

Naturally, the idea of image analysis by auto regression algorithms is not limited to the original image, and can be applied to images that have undergone certain transformations of the original image too, such as linear and non-linear local filters (linear shift invariance filtering, Laplace, gradient filtering, etc.).

Reproducible texture analysis does not require an identification of objects, and is, therefore, not associated with object-related information. It is a second, independent approach to extract diagnosis relevant information from a histological image.  

The identification of objects and the analysis of textures are the two columns of information extraction from a histological image. Man is doing this by appropriate training and gathered experience. How can automatically a machine act in this manner? This machine obviously requires a certain amount of “intelligence”, commonly called artificial intelligence.

Artificial Intelligence in Diagnostic Pathology

The term artificial intelligence has been defined by the American Association for Artificial Intelligence as follows: Artificial Intelligence (AI) is the understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. In principle, it is connected with image information acquisition, transfer and recognition. The term artificial intelligence was raised with the theoretical considerations about computer language and design, and dates back around 50 years to 1956. Since then, AI has made inroads into numerous sciences ranging from physic to philosophy, and from biology to logic. Its main practical application is seen in the realization of versatile industrial robotics; however, it is also applicable to solve questions such as: What is the rationale of a computer program? How can we transfer (visual, acoustic) information into a therapeutic act, i.e. automated diagnosis?

The simplest way to apply AI is the retrieval of previously codified information and the computation of potential associations. Therefore, in diagnostic pathology, AI was formerly involved the appropriate use suitable data bank and retrieval systems including automated language coding [^{157–159 161 247 378}]. In parallel with the technical development of “computing machines” the contribution of AI to design and handling of these machines became more and more significant, and several hierarchically ordered information levels have been implemented. The reason of these strong interactions can be seen in the fact that AI is primarily a technique to understand and normalize any kind of decisions. On the other hand, decisions are based upon statistics. The larger the amount of available data the better is the statistical significance of a past or future decision. Any decision is closely associated with “information extracts of a data set” or-in terms of pathology-the diagnosis. A tissue-based diagnosis is only partly based upon image information. A general theory “how to reliably state a diagnosis based upon histological images” is unavailable to our knowledge; however, the following basic theorems can be given:

1. Diagnosis finding is an algorithm that includes different levels of information. These levels reflect those related to the information transfer within a biological system (i.e., genotype, phenotype, symptoms, etc.).
2. Their contribution to the final statement (diagnosis) depends upon the diagnosis itself. In other words, the performance of a diagnosis can be simulated by an autocorrelation function.
3. The result is “feature-associated” information, and comprises, in addition, statements how to use this information (implicit instructions to the patient’s physician);
4. A significant compartment of its parameters can be “extracted” from at least two different features of an image seen in a histological slide, namely from its texture (connective segmented areas), and its basic units (segmented disconnected areas).
5. It includes a decision making process that is to a significant extent based upon image segmentation. In other words, the performance of “correct” image segmentation is the prerequisite for a “correct” diagnosis.
6. All image “qualities” which are associated image segmentations will also influence the diagnosis.
7. Finally, AI seems to provide adequate theoretical and technical methods to analyze the performance of image segmentation. Therefore, it should be able to be directly used for the assessment of tissue-based diagnosis.

Several publications describe and analyze the use of AI in decision making processes [^{34,36–38,33,155,203,205}]. The applied techniques include Bayes decision formula, discriminate analysis, or neural networks. Usually, various information levels make contributions. The computation combines clinical data, such as sex, age, symptoms, with quantitative image data such as tumor cell grading, number of mitoses, or orientation of cell nuclei. The derived diagnosis commonly focuses on specific aims, for example, to analyze histopathological effects of androgen deprivation on prostate cancer [^345,346]. The future development of AI in virtual slide technology will probably contribute in a different manner: It will be used to “analyze” variations of input data and their influence on the result. A characteristic aim is to search for image areas of interest for further diagnosis investigations [^{231,236,241,253}]. The algorithm can be implemented in image acquisition systems, i.e. used to construct “smart image acquisition machines”, and to integrate image features (texture, objects) into final diagnosis decision systems [²⁶³]. Virtual slide technology can be used most effectively, if AI is implemented at various stages, starting from image acquisition, going via diagnosis assistance in search for specific areas, textures and elements, and finally approaching the definite diagnosis and its supervision by statistical estimations of probability, benefits and potential hazards of the stated diagnosis for the individual patient.

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