Nuclei segmentation for computer-aided diagnosis of breast cancer

Abstract

Breast cancer is the most common cancer among women. The effectiveness of treatment depends on early detection of the disease. Computer-aided diagnosis plays an increasingly important role in this field. Particularly, digital pathology has recently become of interest to a growing number of scientists. This work reports on advances in computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. The task at hand is to classify those as either benign or malignant. We propose a robust segmentation procedure giving satisfactory nuclei separation even when they are densely clustered in the image. Firstly, we determine centers of the nuclei using conditional erosion. The erosion is performed on a binary mask obtained with the use of adaptive thresholding in grayscale and clustering in a color space. Then, we use the multi-label fast marching algorithm initialized with the centers to obtain the final segmentation. A set of 84 features extracted from the nuclei is used in the classification by three different classifiers. The approach was tested on 450 microscopic images of fine needle biopsies obtained from patients of the Regional Hospital in Zielona Góra, Poland. The classification accuracy presented in this paper reaches 100%, which shows that a medical decision support system based on our method would provide accurate diagnostic information.

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

“…Handcrafted features, such as texture, topological and morphological, are employed by Kowel et al for training a classifier using 500 images, representing 50 patients. They attained 84%-90% accuracy [31]. A circular Hough transform and Otsu-threshold techniques were performed by Filipczuk and George for extracting the nuclei-related texture, shape and features [32].…”

Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy

“…There are numerous reports on applying different imaging techniques (such as microscopic analysis [19], mammography [61] or magnetic resonance [49]), segmentation methods [38] or classification approaches [20] for this task. However, not much attention was paid to the problem of designing a decision support system for breast cancer malignancy grading [36].…”

An accurate fingerprint reference point determination method based on curvature estimation of separated ridges

“…Recognition systems, classifiers, as well as self-adaptive classifiers (Porwik et al, 2016;Krawczyk and Woźniak, 2016), are developed and applied in many domains, e.g., electronics, biometrics (Putz-Leszczyńska, 2015;Pujol et al, 2016), medicine (Porwik et al, 2009;Porwik and Doroz, 2014;Koprowski, 2016;Kowal and Filipczuk, 2014;Mazurek and Oszutowska-Mazurek, 2014). Of the many biometric techniques, fingerprint identification is most prevalent, be it as a tool in police work and the courts, or in a range of commercial applications: banking, security systems, etc.…”