A Method For Improve Preprocessing Images Mammography

Abstract

A great volume of mammography images have dark background, these parts are not important in processing of mammography images. We can decrease the picture size and increase the processing speed by deleting these parts. Some parts of images have some notes and labels which consist of information like name, family, hospital's seal, date and…. It is better to omit these labels before processing since they are bright; the same as gray level of some breast tissues and have the same gray level. In the other way it is better to limit processing to the breast region and omit excessive parts. Previous studies over breast tissues show that if the considered region has not been marked clearly, it will influence characteristics and will not give the desired result. For obtaining this aim first find the main breast region, and then omit excessive parts to obtain processing result accurately and rapidly. The work that we do in this paper is introduced in three phases. The first phase is omitting the excessive image parts which are in the two sides of the image; we do this work by the usage of the pixels brightness. The second phase is the distinction of the breast direction and put all images in one direction; we do this work by the usage of threshold limit of gray level of the two halves of the image. The third phase is the breast region segmentation from the background; we do this work by the usage of series of point operations and the growing region method and the result has been reported to 99%. false negative mammograms (28). It has been shown that an independent second reading of mammograms improves the sensitivity of mammography by as much as 15% (29). Computerized analysis of mammograms has been envisaged as a means of providing a emulated secon- opinion (30), improving consistency by providing a standardized approach lo mammogram interpretation, and increasing detection sensitivity. Computer- Aided Detection (CADe) is the process of identifying potential abnormalities withm a mammogram, classifying regions of a mammogram as positive or negative. CADe systems use image processing algorithms to analyze mammograms for possible abnormalities including masses, microcalcifications, distortions in breast architecture, and symmetric densities. AAer an initial examination of the mammographic images, a radiologist reviews suspicious regions prompted by the CADe system and determines whether they warrant further follow-up. The CADe system assists the radiologist by confirming the detection of suspicious regions or identifying those that might otherwise have been missed. The precise segmentation of the breast region in mammograms is an essential preprocessing step in the computerized analysis of mammograms. It allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. It also facilitates enhancements to techniques such as comparative analysis, which includes the automated comparison of corresponding mammograms. The breast boundary contains significant information relating to the deformation between two mammograms and is the source of information for relating the position of the nipple relative to the skin surface.