Histopathological Image Analysis: A Review <s> C. Grade-Based Classification of Histopathology Imagery <s> In this paper, wavelets were employed for multiscale image analysis to extract parameters for the description of chromatin texture in the cytological diagnosis and grading of invasive breast cancer. Their value was estimated by comparing the performance of co-occurrence, densitometric, and morphometric parameters in an automated K-nearest neighbor (Knn) classification scheme based on light microscopic images of isolated nuclei of paraffin-embedded tissue. This design allowed a multifaceted cytological retrospective study of which the practical value can be judged easily. Results show that wavelets perform excellently with classification scores comparable with densitometric and co-occurrence features. Moreover, because wavelets showed a high additive value with the other textural groups, this panel allowed a very profound description with higher recognition scores than previously reported (76% for individual nuclei, 100% for cases). Morphometric parameters performed less well and only slightly increased correct classification. The major drawback, besides image segmentation errors demanding operator supervision, emanated to be the few false-negative cases, which restrict the immediate practical use. However, an enlargement of the parameter set may avoid this misclassification, resulting in an applicable expert system of practical use. Cytometry 33:32–40, 1998. © 1998 Wiley-Liss, Inc. <s> BIB001 </s> Histopathological Image Analysis: A Review <s> C. Grade-Based Classification of Histopathology Imagery <s> The development of an automated algorithm for the categorization of normal and cancerous colon mucosa is reported. Six features based on texture analysis were studied. They were derived using the co-occurrence matrix and were angular second moment, entropy, contrast, inverse difference moment, dissimilarity, and correlation. Optical density was also studied. Forty-four normal images and 58 cancerous images from sections of the colon were analyzed. These two groups were split equally into two subgroups: one set was used for supervised training and the other to test the classification algorithm. A stepwise selection procedure showed that correlation and entropy were the features that discriminated most strongly between normal and cancerous tissue (P<0.0001). A parametric linear-discriminate function was used to determine the classification rule. For the training set, a sensitivity and specificity of 93.1% and 81.8%, respectively, were achieved, with an overall accuracy of 88.2%. These results mere confirmed with the test set, with a sensitivity and specificity of 93.1% and 86.4%, respectively, and an overall accuracy of 90.2%. <s> BIB002 </s> Histopathological Image Analysis: A Review <s> C. Grade-Based Classification of Histopathology Imagery <s> The histological grading of cervical intraepithelial neoplasia (CIN) remains subjective, resulting in inter- and intra-observer variation and poor reproducibility in the grading of cervical lesions. This study has attempted to develop an objective grading system using automated machine vision. The architectural features of cervical squamous epithelium are quantitatively analysed using a combination of computerized digital image processing and Delaunay triangulation analysis; 230 images digitally captured from cases previously classified by a gynaecological pathologist included normal cervical squamous epithelium (n=30), koilocytosis (n=46), CIN 1 (n=52), CIN 2 (n=56), and CIN 3 (n=46). Intra- and inter-observer variation had kappa values of 0.502 and 0.415, respectively. A machine vision system was developed in KS400 macro programming language to segment and mark the centres of all nuclei within the epithelium. By object-oriented analysis of image components, the positional information of nuclei was used to construct a Delaunay triangulation mesh. Each mesh was analysed to compute triangle dimensions including the mean triangle area, the mean triangle edge length, and the number of triangles per unit area, giving an individual quantitative profile of measurements for each case. Discriminant analysis of the geometric data revealed the significant discriminatory variables from which a classification score was derived. The scoring system distinguished between normal and CIN 3 in 98.7% of cases and between koilocytosis and CIN 1 in 76.5% of cases, but only 62.3% of the CIN cases were classified into the correct group, with the CIN 2 group showing the highest rate of misclassification. Graphical plots of triangulation data demonstrated the continuum of morphological change from normal squamous epithelium to the highest grade of CIN, with overlapping of the groups originally defined by the pathologists. This study shows that automated location of nuclei in cervical biopsies using computerized image analysis is possible. Analysis of positional information enables quantitative evaluation of architectural features in CIN using Delaunay triangulation meshes, which is effective in the objective classification of CIN. This demonstrates the future potential of automated machine vision systems in diagnostic histopathology. Copyright © 2000 John Wiley & Sons, Ltd. <s> BIB003 </s> Histopathological Image Analysis: A Review <s> C. Grade-Based Classification of Histopathology Imagery <s> Histological grading of pathological images is used to determine the level of malignancy of cancerous tissues. This is a very important task in prostate cancer prognosis, since it is used for treatment planning. If infection of cancer is not rejected by noninvasive diagnostic techniques like magnetic resonance imaging, computed tomography scan, and ultrasound, then biopsy specimens of tissue are tested. For prostate, biopsied tissue is stained by hematoxyline and eosine method and viewed by pathologists under a microscope to determine its histological grade. Human grading is very subjective due to interobserver and intraobserver variations and in some cases difficult and time-consuming. Thus, an automatic and repeatable technique is needed for grading. The Gleason grading system is the most common method for histological grading of prostate tissue samples. According to this system, each cancerous specimen is assigned one of five grades. Although some automatic systems have been developed for analysis of pathological images, Gleason grading has not yet been automated; the goal of this research is to automate it. To this end, we calculate energy and entropy features of multiwavelet coefficients of the image. Then, we select most discriminative features by simulated annealing and use a k-nearest neighbor classifier to classify each image to appropriate grade (class). The leaving-one-out technique is used for error rate estimation. We also obtain the results using features extracted by wavelet packets and co-occurrence matrices and compare them with the multiwavelet method. Experimental results show the superiority of the multiwavelet transforms compared with other techniques. For multiwavelets, critically sampled preprocessing outperforms repeated-row preprocessing and has less sensitivity to noise for second level of decomposition. The first level of decomposition is very sensitive to noise and, thus, should not be used for feature extraction. The best multiwavelet method grades prostate pathological images correctly 97% of the time. <s> BIB004 </s> Histopathological Image Analysis: A Review <s> C. Grade-Based Classification of Histopathology Imagery <s> We present a study of image features for cancer diagnosis and Gleason grading of the histological images of prostate. In diagnosis, the tissue image is classified into the tumor and nontumor classes. In Gleason grading, which characterizes tumor aggressiveness, the image is classified as containing a low- or high-grade tumor. The image sets used in this paper consisted of 367 and 268 color images for the diagnosis and Gleason grading problems, respectively, and were captured from representative areas of hematoxylin and eosin-stained tissue retrieved from tissue microarray cores or whole sections. The primary contribution of this paper is to aggregate color, texture, and morphometric cues at the global and histological object levels for classification. Features representing different visual cues were combined in a supervised learning framework. We compared the performance of Gaussian, -nearest neighbor, and support vector machine classifiers together with the sequential forward feature selection algorithm. On diagnosis, using a five-fold cross-validation estimate, an accuracy of 96.7% was obtained. On Gleason grading, the achieved accuracy of classification into low- and high-grade classes was 81.0%. <s> BIB005
The classification of histopathology imagery is often the ultimate goal in image analysis, particularly in cancer applications. Features derived from segmented nuclei and glands from histopathology are usually a prerequisite to extracting higher level information regarding the state of the disease. For instance, the grading of prostate cancer by Jafari-Khouzani and Soltanian-Zadeh BIB004 yielded 97% accuracy for H&E stained imagery based on features derived from nuclear structures in histopathology. Weyn et al. BIB001 in clasifying nuclei as benign or malignant, but 100% classification on a patient level. Tabesh et al. BIB005 found 96.7% accuracy in discriminating between prostate tissue slides with cancer and no cancer, and 81% accuracy in the discrimination between low and high Gleason grades in the same imagery. Immunohistochemically stained colon mucosa allowed for an accuracy of 92% in classification of benign and malignant images by Esgiar et al. BIB002 . The classification of histopathology imagery using spatial architecture information as presented in Weyn et al. BIB001 resulted in 88.7%-96.8% accuracy in the diagnosis of lung cancer, 94.9% accuracy in the typing of malignant mesothelioma, and 80.0%-82.9% accuracy in the prognosis of malignant mesothelioma for Feulgen-stained lung sections. The analysis of H&E stained brain tissue by Demir et al.  gave 95.5%-97.1% accuracy in the discrimination between benign and cancerous tissue. Keenan et al. BIB003 reported accuracies of 62.3%-76.5% in the grading of H&E stained cervical tissue. Fig. 15(a) shows the low dimensional embedding of the high dimensional attribute space via locally linear embedding of 20 images representing prostate cancer grades 3 (green circles) and 4 (blue squares). Each image is displayed as a point in 3D eigenspace. The clustering clearly shows very good discrimination between these 2 classes which clinically is the most challenging problem in terms of Gleason grading. Fig. 15(b) shows bar plots reflecting the classification accuracy obtained via a supervised classifier in distinguishing between pairs of tissue classes -grade 3\4, grade 3 vs. benign epithelium, and grade 4 vs. benign epithelium via a SVM classifier. Note that in every case the classification accuracy is over 90%.