Automatic classification of colorectal and prostatic histologic tumor images using multiscale multispectral local binary pattern texture features and stacked generalization

Abstract

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Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images

“…Unfortunately, the histological assessment of pathology sections is highly subjective and prone to inter/intra observer variation ( 2 , 7 – 9 ), motivating the need for automated or computer-aided diagnosis of pathological slides. Recently, more emphasis has been placed on using digital technologies to produce high-resolution whole slide images (WSI) ( 10 ). Texture feature extraction from digital pathology images has been a major focus in the development of computer-aided diagnosis systems ( 10 – 12 ).…”

“…Recently, more emphasis has been placed on using digital technologies to produce high-resolution whole slide images (WSI) ( 10 ). Texture feature extraction from digital pathology images has been a major focus in the development of computer-aided diagnosis systems ( 10 – 12 ). An early study conducted by Esgiar et al used correlation and entropy texture features computed from gray-level co-occurrence matrices (GLCM) to differentiate between normal and cancerous tissue ( 13 ).…”

“…To our knowledge, this is the first work to investigate the link between scale textures in multispectral images and their association with stages of CRC malignancy. Multiscale texture features derived from multispectral images encode thousands of invisible patterns that are complementary to traditional texture based on GLCM, local binary patterns, Laplacian of Gaussian filter, deep learning ( 10 – 12 , 21 ), or shape measurements ( 22 ). In this context, multiscale texture features measure the heterogeneity in the images of the PT and could be one of the radiomic techniques.…”

Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

“…The GLCM is also successfully applied as a technique for feature extraction on histopathological images and is also combined with local binary patterns, called LBGLCM (Öztürk and Akdemir, 2018). Another research is proposed by Peyret et al (2018) propose the multiscale multispectral local binary pattern and combine it with GLCM for histology tumor classification. The images are classified by Support Vector Machine and obtain the highest accuracy (%) up to 99.6±0.4 for Qatar dataset (Peyret et al, 2018).…”

“…Another research is proposed by Peyret et al (2018) propose the multiscale multispectral local binary pattern and combine it with GLCM for histology tumor classification. The images are classified by Support Vector Machine and obtain the highest accuracy (%) up to 99.6±0.4 for Qatar dataset (Peyret et al, 2018).…”