Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning

Abstract

The Gleason grading system is generally used for histological grading of prostate cancer. In this paper, we first introduce using the Shearlet transform and its coefficients as texture features for automatic Gleason grading. The Shearlet transform is a mathematical tool defined based on affine systems and can analyze signals at various orientations and scales and detect singularities, such as image edges. These properties make the Shearlet transform more suitable for Gleason grading compared to the other transform-based feature extraction methods, such as Fourier transform, wavelet transform, etc. We also extract color channel histograms and morphological features. These features are the essential building blocks of what pathologists consider when they perform Gleason grading. Then, we use the multiple kernel learning (MKL) algorithm for fusing all three different types of extracted features. We use support vector machines (SVM) equipped with MKL for the classification of prostate slides with different Gleason grades. Using the proposed method, we achieved high classification accuracy in a dataset containing 100 prostate cancer sample images of Gleason Grades 2-5.Gland units contain epithelial cells around the lumen. When cancer happens, epithelial cells randomly duplicate, disturbing the normal structure of glands, as shown in Figure 2b,c.

Microscopic medical image classification framework via deep learning and shearlet transform

“…We previously used HSCs for breast cancer detection 9 and prostate cancer detection 11 and Gleason grading. 10,12 Figure 4 shows the histogram of magnitude of shearlet coefficients for two cases. Figure 4(a) shows the HSCs for a pair of benign and malignant images, where they were correctly classified.…”

“…[5][6][7][8] Wavelets do not have directional sensitivity, which makes them unsuitable for detecting directional features. That was the motive for using shearlets instead of wavelets in our previous studies [9][10][11][12] and in this paper as well. On the other hand, recent feature learning methods have gained a lot of attention due to the success of deep neural networks methods in computer vision applications.…”

“…Some other techniques that used hand-crafted features are based on texture, color, and morphological features. [7][8][9][10][11][12] Jafari-Khouzani and Soltanian-Zadeh 7 extracted energy and entropy from multiwavelets coefficients and used them for the task of automatic Gleason grading of prostate. They used the k-nearest neighbor algorithm to classify images.…”

“…We also used HSC for prostate cancer detection of histological images 10 and magnetic resonance (MR) images 11 and achieved 100% and 97% classification accuracy, respectively. In a recent study, 12 we extracted multiple features from the histological images of prostate cancer and used the multiple kernel learning (MKL) algorithm for fusing the features. Then we used SVM equipped with MKL for the classification of prostate slides with different Gleason grades.…”

Detection and classification of thyroid nodule using Shearlet coefficients and support vector machine

“…Hence shearlet based texture feature descriptors can characterize thyroid nodules well. Discrete shearlet coefficients of the images in the dataset are calculated and co-occurrence matrix of the Shearlet coefficients [17] is computed from each image. This will give the information about the texture of the images, since Shearlet coefficients are good representatives of the heterogeneity of images.…”