Open AccessUnsupervised Segmentation of Breast Infrared Images in Lateral View Using Histogram of Oriented Gradients
Author(s) -
Thays Correa,
Fabíola de Oliveira,
Matheus de Freitas Oliveira Baffa,
Lucas Lattari
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wvc.2020.13477
Subject(s) - segmentation , artificial intelligence , histogram , computer science , region of interest , pattern recognition (psychology) , computer vision , image segmentation , breast cancer , histogram of oriented gradients , region growing , sample (material) , scale space segmentation , image (mathematics) , cancer , medicine , chemistry , chromatography
Breast cancer is the second most common type of cancer in the world. It is estimated that 29.7% of new cases diagnosed in Brazil occur in any structures of the breasts. However, the disease has a good prognosis if detected early. Thus, the development of new technologies to help doctors to provide an accurate diagnosis is indispensable. The goal of this work is to develop a new method to automate parts of computer-aided diagnosis systems, performing the unsupervised segmentation of the Region of Interest (ROI) of infrared breast images acquired in lateral view. The segmentation proposed in this paper consists of three stages. The first stage pre-processes the infrared images of the lateral region of breasts. Later, features are extracted from a descriptor based on Histogram of Oriented Gradients (HOG). Concluding, a Machine Learning algorithm is used to perform the segmentation of the sample. The current method obtained an average of 89.9% accuracy and 94.3% specificity in our experiments, which is promising compared to other works.