Adaptive hysteresis thresholding segmentation technique for localizing the breast masses in the curve stitching...

Massive work by distinguished researchers in the domain of breast segmentation has been proposed. However, no significant solution reduces the limitations of the false positive rate of cancerous cells in the breast body for probing the abnormalities of particular features. This problem is challenging in its nature and essential to be solved. It is needed to reach the optimal measurements of the breast parenchyma, the breast patchy regions of the mammogram, or the breast registration for searching of precise oddities.

In this work, we propose a novel approach for observing the abnormal breast cells identification with high sensitivity. A cancer tumor often produces a specific protein in the blood that serves as a marker for the cancer cells. These cells break off from the cancer and move into the blood stream. However, presence of pectoral muscle in breast mammogram highly affects the detection process of breast tumor. A novel aspect of the proposed method is that the curve stitching technique is developed for removing of pectoral muscle. Following this, an adaptive hysteresis thresholding is used for segmentation. This hybrid technique is used for segmenting a breast region of digital mammogram with suppression of pectoral muscle.

The proposed method attains a highest sensitivity rate of 96.6% for the MIAS dataset and 96.4% for the DDSM dataset as compared to existing methods.

The main idea behind this is to improve the threshold based segmentation techniques to create an adaptive threshold and apposite templates, in order to conserve tumor salient features about suspicious regions to classify benign and malignant mass in mammogram. First, a spline based curve fitting is applied on edges of the breast parenchyma and fill the region with a very low intensity value and map on original image to preserve the original intensity of breast region free of pectoral muscle. The results of the experiments show that the proposed segmentation technique is efficient when tested on MIAS and DDSM dataset.