Frequential versus spatial colour textons for breast TMA classification

Machine Learning Methods for Histopathological Image Analysis: A Review

Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research.

Medical Image Retrieval Using Multi-Texton Assignment

In this paper, we present a multi-texton representation method for medical image retrieval, which utilizes the locality constraint to encode each filter bank response within its local-coordinate system consisting of the k nearest neighbors in texton dictionary and subsequently employs spatial pyramid matching technique to implement feature vector representation. Comparison with the traditional nearest neighbor assignment followed by texton histogram statistics method, our strategies reduce the quantization errors in mapping process and add information about the spatial layout of texton distributions and, thus, increase the descriptive power of the image representation. We investigate the effects of different parameters on system performance in order to choose the appropriate ones for our datasets and carry out experiments on the IRMA-2009 medical collection and the mammographic patch dataset. The extensive experimental results demonstrate that the proposed method has superior performance.

Automatic quantification of IHC stain in breast TMA using colour analysis

Immunohistochemical (IHC) biomarkers in breast tissue microarray (TMA) samples are used daily in pathology departments. In recent years, automatic methods to evaluate positive staining have been investigated since they may save time and reduce errors in the diagnosis. These errors are mostly due to subjective evaluation. The aim of this work is to develop a density tool able to automatically quantify the positive brown IHC stain in breast TMA for different biomarkers. To avoid the problem of colour variation and make a robust tool independent of the staining process, several colour standardization methods have been analysed. Four colour standardization methods have been compared against colour model segmentation. The standardization methods have been compared by means of NBS colour distance. The use of colour standardization helps to reduce noise due to stain and histological sample preparation. However, the most reliable and robust results have been obtained by combining the HSV and RGB colour models for segmentation with the HSB channels. The segmentation provides three outputs based on three saturation values for weak, medium and strong staining. Each output image can be combined according to the type of biomarker staining. The results with 12 biomarkers were evaluated and compared to the segmentation and density calculation done by expert pathologists. The Hausdorff distance, sensitivity and specificity have been used to quantitative validate the results. The tests carried out with 8000 TMA images provided an average of 95.94% accuracy applied to the total tissue cylinder area. Colour standardization was used only when the tissue core had blurring and fading stain and the expert could not evaluate them without a pre-processing.

Influence of Texture and Colour in Breast TMA Classification

Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors.