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- Junqueira’s Basic Histology: Text and Atlas, 15e | AccessMedicine | McGraw-Hill Medical
In Colombia, prostate cancer is the most common cancer in men and it is also one of the leading causes of death by cancer more than new cases and deaths per year This type of cancer is graded from 1 to 5 using Gleason grading method, and it is based on structural features of the tissue, where Grade 1 has well defined patterns, while 5 grade patterns are difficult to differentiate.
Automatic grading can be performed in two stages, a segmentation process to extract glands from the background and to get morphological measures from them, and then train a classifier to recognize their patterns and perform annotation of each grade. After that, morphological features of segmented regions are used as input for an LDA classifier 7 convolved the image with a Gaussian Kernel, using peaks as seeds for a region growing procedure.
Glands area are estimated and used jointly with a MRF Markov Random Field to train a Bayesian estimator and classify glands as either malignant or benign.
Then morphological and topological features were extracted from regions and used to train a binary Support Vector Machine SVM model, for classifying between 3 and 4 grades. Another approach using extracted features directly from image into a classification model has been applied in this domain. This approach commonly uses texture features like co-occurrence matrix 24 , Haar wavelets 49 and text on histogram Cervix cancer is the most prevalent cancer in Colombia, Cervix cancer refers to cancer forming tissues of the uterine cervix, and is graded into three categories: cervical intraepithelial neoplasia CIN 1, CIN 2 and CIN 3 corresponding to mild, moderate and severe dysplasia.
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In 52 segmentation was performed with a supervised model using texture features and an SVM classifier at different resolutions, after that, morphological measures of nuclei are used to train a multiclass SVM model with RBF Kernel and to grade cervix cancer 5 proposed a gabor-filter-based segmentation with a supervised learning approach, resulting a pixel-by-pixel classification into four classes: basal, stroma, normal and abnormal cell. Then, clustering and median filters are applied to count normal and abnormal nuclei.
Finally, ratio between normal and abnormal is used as discriminant feature to grade the image. On the other hand, a standalone segmentation was proposed in 53 to identify cervical nuclei based on HSV color and morphological features, while 54 applied GMM Gaussian Mixture Models and grayscale features. It is the third most common cancer in the world for both women and men, in Colombia it is the fourth for both. Also, it is the fifth in Colombia leading causes of death for cancer. Assessment of cancer grading is based on visual abnormalities found by pathologists; this grade is subjective because it depends on interpretation given by the expert.
Automatic methods have been proposed to detect and grade colon cancer.
Training images are represented with these templates sequences, then a markovian model is trained with such representations. Experiments showed the proposed model performs better than raw features, even when there was less data to train. A segmentation approach was shown in 34 using five measures of Haralick texture features, to detect three types of cells: carcinoma, benign and intraepithelial neoplasia, using a neural network as classifier obtaining at the end a computational simplicity of segmentation which is performed in a very short time 6 presented an unsupervised object-oriented segmentation algorithm based on texture features.
K-means clustering is performed on the color intensities to detect and define objects referred to connective tissues, luminal structures and epithelial cell components. Two homogeneity measures were defined, object size uniformity and object spatial distribution uniformity, as textures features for each object, finally a region-growing algorithm is carried out to completed segmentation process. The proposed method was compared against JSEG algorithm, a pixel-oriented approach 55 , obtaining better performance in terms of specificity and sensitivity.
It has different risk factors and its development is mainly due to ultraviolet radiation exposure.
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In this evaluation, physicians aim to recognize some characteristic patterns or complex mixes of patterns. This process is called differential diagnosis and it is mainly achieved by visual analysis. In 57 , the structural patterns that characterize the basal-cell carcinoma are described and correspond to 11 different complex patterns.
This kind of representation can be extended to analyze semantic concepts by identifying visual patterns in BCC images 38 , In order to support medical searching within BCC image collections, 58 proposed a method to ease its visualization and exploration using BOF with texture features. Other methods for automatic annotation for BCC collections has been proposed, 59 , 60 applied NMF with BOF to build a latent topic model with probabilistic support as main advantage for interpretability of results.
A proper determination of Regions of interest ROI would allow to concentrate any processing effort on specific image areas to diagnose BCC disease, to find such RoIs 61 proposed a supervised model inspired by visual cortex areas of the brain and the way their perceive the world.
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The most important challenge is that unlike natural images where high-level semantic concepts are related with connected areas and objects, in histopathology images high-level semantic interpretations are related to pathological lesions, morphological and architectural features, structural configuration, cells and biological patterns organization context , and not only the presence of patterns is important but also their absence is. Such factors depend on the amount of magnification and the type of organ, which encompass a complex mixture of visual patterns that allow deciding about the illness presence.
Dealing automatically with these factors is still an open problem On the other hand, thanks to recent advances in microscopical acquisition technology scanners and robotic microscopes it has been possible to collect huge numbers of histopathology images and make them publicly available through publicly accessible image databases Table 2 lists different open histopathology image databases accessible through the web. This trend is very positive for the progress of digital histopathology research, since it allows objective comparison of methods and strategies.
However, standard evaluation protocols are required. An example effort in this direction is the publication of the MITOS dataset, by the International Conference on Pattern Recognition ICPR , along with a set of evaluation metrics to encourage participation on their contest where the objective was to evaluate methods to automatically determine the mitotic count.
Nowadays one of the new challenges is to deal the growing size of these image collections from hundred thousands to millions and whole-slide-images sizes 20 GB or more This phenomena is known as Big Data, and in pathology domain is opening new challenges, opportunities and approaches which are being addressed by a new area called Digital Pathology Finally, It should be noticed most of these Finally, It should be noticed most of these methods and frameworks reviewed in this work are addressed to support and to empower the diagnosis of pathologists rather than replace them Computational pathology: challenges and promises for tissue analysis.
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Living with the semantic gap: Experiences and remedies in the context of medical imaging. Hewitson T, Darby IA. Histology Protocols.
Junqueira’s Basic Histology: Text and Atlas, 15e | AccessMedicine | McGraw-Hill Medical
National University of Colombia. Kiernan J. Histological and Histochemical Methods: Theory and Practice. Cold Spring Harbor Laboratory Press; Lowe DG. Object recognition from local scale-invariant features. In Computer vision, The proceedings of the seventh IEEE international conference. Alpaydin E.
Introduction to Machine Learning. The MIT Press; A resampling-based Markovian model for automated colon cancer diagnosis. IEEE transactions on bio-medical engineering. Demir C, Yener B. Automated cancer diagnosis based on histopathological images: a systematic survey.
Rensselaer Polytechnic Institute. An efficient computational framework for the analysis of whole slide images: Application to follicular lymphoma immunohistochemistry.
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Journal of Computational Science. Mete M, Topaloglu U. Statistical comparison of color model-classifier pairs in hematoxylin and eosin stained histological images. Mahmoud-Ghoneim D. Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions.
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Journal of Pathology Informatics. Content-based image retrieval utilizing explicit shape descriptors: applications to breast MRI and prostate histopathology. A boosted classifier for integrating multiple fields of view: Breast cancer grading in histopathology.