Suspicious-Region Segmentation From Breast Thermogram Using DLPE-Based Level Set Method.

Segmentation of suspicious regions (SRs) of a thermal breast image (TBI) is a very significant and challenging problem for the identification of breast cancer. Therefore, in this work, we have proposed an active contour model for the segmentation of the SRs in TBI. The proposed segmentation method combines three significant steps. First, a novel method, called smaller-peaks corresponding to the high-intensity-pixels and the centroid-knowledge of SRs (SCH-CS), is proposed to approximately locate the SRs, whose contours are later used as the initial evolving curves of the level set method (LSM). Second, a new energy functional, called different local priorities embedded (DLPE), is proposed regarding the level set function. DLPE is then minimized using the interleaved level set evolution to segment the potential SRs in TBI more accurately. Finally, a new stopping criterion is incorporated into the proposed LSM. The proposed LSM not only increases the segmentation speed but also ameliorates the segmentation accuracy. The performance of our SR segmentation method was evaluated on two TBI databases, namely, DMR-IR and DBT-TU-JU, and the average segmentation accuracies obtained on these databases are 72.18% and 71.26% respectively, which are better than the other state-of-the-art methods. Beside this, a novel framework to analyze TBIs is proposed for differentiating abnormal and normal breasts on the basis of the segmented SRs. We have also shown experimentally that investigating only the SRs instead of the whole breast is more effective in differentiating abnormal and normal breasts.

Patch-based system for Classification of Breast Histology images using deep learning.

In this work, we proposed a patch-based classifier (PBC) using Convolutional neural network (CNN) for automatic classification of histopathological breast images. Presence of limited images necessitated extraction of patches and augmentation to boost the number of training samples. Thus patches of suitable sizes carrying crucial diagnostic information were extracted from the original images. The proposed classification system works in two different modes: one patch in one decision (OPOD) and all patches in one decision (APOD). The proposed PBC first predicts the class label of each patch by OPOD mode. If that class label is the same for all the extracted patches and that is the class label of that image, then the output is considered as correct classification. In another mode that is APOD, the class label of each extracted patch is extracted as done in OPOD and a majority voting scheme takes the final decision about class label of the image. We have used ICIAR 2018 breast histology image dataset for this work which comprises of 4 different classes namely normal, benign, in situ and invasive carcinoma. Experimental results show that our proposed OPOD mode achieved a patch-wise classification accuracy of 77.4% for 4 and 84.7% for 2 histopathological classes respectively on the test set obtained by splitting the training dataset. Also, our proposed APOD technique achieved image-wise classification accuracy of 90% for 4-class and 92.5% for 2-class classification respectively on the split test set. Further, we have achieved accuracy of 87% on the hidden test dataset of ICIAR-2018.