Detection of microcalcification clusters in digitized X-ray mammograms using unsharp masking and image statistics

Abstract

A fully automated method for detecting microcalcification (MC) clusters in regions of interest (ROIs) extracted from digitized X-ray mammograms is proposed. In the first stage, an unsharp masking is used to perform the contrast enhancement of the MCs. In the second stage, the ROIs are decomposed into a 2-level contourlet representation and the reconstruction is obtained by eliminating the low-frequency subband in the second level. In the third stage, In particular, a true positive rate of about 94% is achieved at the rate of 0.06 false positives per image.

Effective texture features in mammogram images via multi-roi segmentation

“…Recent years have seen a proliferation of mammogram segmentation algorithms developed by a number of different teams of researchers. There are a wide variety of segmentation methods that may be applied to an image, such as region growth [14], Markov Random Fields [11], fractal modelling [2], tree structured wavelet transform [12], adaptive density-weighted contrast enhancement [9], and morphological operations [10]. Heuristics are the determining factor, based on characteristics of the subset of the focus region.…”

Perbaikan Tingkat Kekaburan Gambar Akibat Pembesaran Pada Hasil Creenshot Dengan Metode Unsharp Mask

“…Berbagai metode dalam perbaikan citra untuk melakukan ketajaman dan kehalusan citra yang digunkan adalah metode unsharp mask [3][4] [5]. Salah teknik yang digunakan untuk mempertajam suatu citra berdasarkan tingkat blur gambar adalah metode unsharp mask [4] [6].…”