Thermal Image Processing for Breast Symmetry Detection Oriented to Automatic Breast Cancer Analysis

Abstract

The present work presents a methodology to automatically detect the symmetry point of breast. In order to achieve this goal, the algorithm corrects thermal image tilt to find a breast symmetry axis, compute a modified symmetric index that can be used as a measure of image quality, breast cosmetic and pathologic issues, and a seed location for a former algorithm reported to automatically achieve breast cancer analysis. The methodology involves filtering, edge detection, windowing edge analysis and shape detection based on the Hough transform. Experimental results show that the proposed method is able to define the symmetry axis as precise as a person do, and correctly detected breast areas in 100% of the cases considered, allowing automatic breast analysis by a previous algorithm.

Application of the Machine Vision Technology and Infrared Thermography to the Detection of Hoof Diseases in Dairy Cows: A Review

“…Resolution of Thermal Images [64] 60 × 80 (0.005 MP) [65,66] 160 × 120 (0.019 MP) [67][68][69][70][71][72][73][74][75][76][77][78][79][80] 320 × 240 (0.077 MP) [63,81,82] 320 × 256 (0.085 MP) [83,84] 336 × 256 (0.086 MP) [85,86] 384 × 288 (0.111 MP) [59][60][61][62][87][88][89][90][91][92][93][94] 640 × 480 (0.307 MP) [95,96] 640 × 512 (0.328 MP)…”

“…Linear filters are applied using convolution, i.e., the new value of each pixel is calculated based on the pixel values in the neighboring region of the original pixel and the kernel coefficients of the operator, which is usually a square matrix of values with an odd number of rows and columns [98]. A common operation leading to image enhancement is noise reduction, in which, for example, a Gaussian filter is usually applied, which belongs to the group of linear filters and reduces noise very effectively by smoothing the image at the expense of fine details [66,78]. The main representative of nonlinear filtering techniques is the median filter [94], which replaces the value of each point of the original image with the median of the values of the points that surround it, thus eliminating isolated noise points [103].…”

“…Knapik and Cyganek used K-means segmentation to reduce the number of prototype symptom descriptors [63]. Another type of segmentation method is edge detection [92], with its typical examples including Sobel, Prewitt, or Canny edge detectors [66,114]. Li et al incorporated a phase matching-based edge detector into their algorithm for automatically matching infrared and visible images [84].…”