A novel algorithm for initial lesion detection in ultrasound breast images

Abstract

This paper proposes a novel approach to initial lesion detection in ultrasound breast images. The objective is to automate the manual process of region of interest (ROI) labeling in computer‐aided diagnosis (CAD). We propose the use of hybrid filtering, multifractal processing, and thresholding segmentation in initial lesion detection and automated ROI labeling. We used 360 ultrasound breast images to evaluate the performance of the proposed approach. Images were preprocessed using histogram equalization before hybrid filtering and multifractal analysis were conducted. Subsequently, thresholding segmentation was applied on the image. Finally, the initial lesions are detected using a rule‐based approach. The accuracy of the automated ROI labeling was measured as an overlap of 0.4 with the lesion outline as compared with lesions labeled by an expert radiologist. We compared the performance of the proposed method with that of three state‐of‐the‐art methods, namely, the radial gradient index filtering technique, the local mean technique, and the fractal dimension technique. We conclude that the proposed method is more accurate and performs more effectively than do the benchmark algorithms considered.PACS numbers: 87.57.Nk

Rapid image stitching and computer‐aided detection for multipass automated breast ultrasound

“…The CAD system's use as a reference opinion for improving accuracy and reliability of diagnosis has attracted much interest among researchers over the past decade. Previous studies focused on two key areas: The detection of the tumor region 22,[38][39][40][41] and the classification of breast masses. [42][43][44][45][46] For boundary extraction of breast masses, Cary et al 38 used leak properties to grow a manually drawn seed region close to the tumor boundary.…”

A novel technique for detecting suspicious lesions in breast ultrasound images

“…An approach proposed in [2] uniquely combines histogram equalization as a preprocessing stage and hybrid filtering, multifractal analysis, thresholding segmentation, and a rule-based approach in fully automated region-of-interest (ROI) labeling as presented in Figure 3. An approach proposed in [2] uniquely combines histogram equalization as a preprocessing stage and hybrid filtering, multifractal analysis, thresholding segmentation, and a rule-based approach in fully automated region-of-interest (ROI) labeling as presented in Figure 3.…”

“…A method for speckle reduction in ultrasonic images using median filtering, Wiener filtering, and wavelet transform was implemented in MATLAB (Natick, MA, United States) by Yap [2]. Median filter is a nonlinear filter that is widely used to replace the original gray level of a pixel by the median of the gray values of pixels in a specific neighborhood.…”

A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis

“…It is one of the most often used segmentation techniques for monochromatic images. Histogram thresholding was used by Yap et al (2008), with relative success, to identify lesion candidates and delimit lesions in BUS images. Other researchers have applied histogram thresholding to pre-processed images.…”