<title>Detection of microcalcifications in mammograms using nonlinear subband decomposition and outlier labeling</title>

Abstract

Computer-aided diagnosis (CAD) will be an important feature of the next generation Picture Archiving and Communication Systems (PACS). In this paper, computer-aided detection of microcalcifications in mammograms using a nonlinear subband decomposition and outlier labeling is examined. The mammogram image is first decomposed into subimages using a nonlinear subband decomposition filter bank. A suitably identified subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. A region with high positive skewness and kurtosis is marked as a region of interest. Finally, an outlier labeling method is used to find the locations of microcalcifications in these regions. Simulation studies are presented.

Statistical textural features for detection of microcalcifications in digitized mammograms

“…is the total sum of elements in the surrounding regiondependence matrix, i.e., (9) and is the reciprocal of the element, which is defined as if otherwise.…”

“…Davies and Dance [6] proposed a local area thresholding process for automatic detection of clustered microcalcifications. Recently, a variety of schemes based on the wavelet transform have been proposed for the detection of microcalcifications [7]- [9]. Some researchers have also studied the potential of CAD to improve the radiologist's performance in detecting of clustered microcalcifications [11], [12].…”

Nonlinear subband decomposition structures in GF-(N) arithmetic

“…Traditionally, linear filters with perfect reconstruction (PR) property are employed for the decomposition. Recently, a variety of nonlinear 'subband' decomposition structures with perfect reconstruction property have been proposed and successfully used in image compression and analysis [4,5,[7][8][9]. The key idea is to replace the linear filters in a classical subband decomposition scheme with nonlinear 'half-band' filters.…”

Automated detection and enhancement of microcalcifications in mammograms using nonlinear subband decomposition

“…In this work, we used the so-called boxplot outlier label- Next, the median, the lower quartile, Q1 and the upper quartile Q3 values are determined [15]. The interquartile range RF is defined to be Q3 -Q1.…”

“…Hence, outlier detection techniques can be used to Figure 1. Nonlinear Subband Decomposition Structure detect the microcalcifications [15]. In this paper, detection is carried out in two steps.…”