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. 2018 Sep 25:2018:5940436.
doi: 10.1155/2018/5940436. eCollection 2018.

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Meenakshi M Pawar  1 , Sanjay N Talbar  2 , Akshay Dudhane  2
Affiliations

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Meenakshi M Pawar et al. J Healthc Eng. .

Abstract

Breast Cancer is the most prevalent cancer among women across the globe. Automatic detection of breast cancer using Computer Aided Diagnosis (CAD) system suffers from false positives (FPs). Thus, reduction of FP is one of the challenging tasks to improve the performance of the diagnosis systems. In the present work, new FP reduction technique has been proposed for breast cancer diagnosis. It is based on appropriate integration of preprocessing, Self-organizing map (SOM) clustering, region of interest (ROI) extraction, and FP reduction. In preprocessing, contrast enhancement of mammograms has been achieved using Local Entropy Maximization algorithm. The unsupervised SOM clusters an image into number of segments to identify the cancerous region and extracts tumor regions (i.e., ROIs). However, it also detects some FPs which affects the efficiency of the algorithm. Therefore, to reduce the FPs, the output of the SOM is given to the FP reduction step which is aimed to classify the extracted ROIs into normal and abnormal class. FP reduction consists of feature mining from the ROIs using proposed local sparse curvelet coefficients followed by classification using artificial neural network (ANN). The performance of proposed algorithm has been validated using the local datasets as TMCH (Tata Memorial Cancer Hospital) and publicly available MIAS (Suckling et al., 1994) and DDSM (Heath et al., 2000) database. The proposed technique results in reduction of FPs from 0.85 to 0.02 FP/image for MIAS, 4.81 to 0.16 FP/image for DDSM, and 2.32 to 0.05 FP/image for TMCH reflecting huge improvement in classification of mammograms.

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Figures

Figure 1
Figure 1
Schematic architecture for automatic breast cancer detection.
Figure 2
Figure 2
Steps for mammogram processing (a) enhanced mammogram, (b) binary mask, (c) pectoral removal, (d) pectoral removed mammogram, (e) clustered image, (f) cluster of interest, and (g) ROI extraction.
Figure 3
Figure 3
Preprocessing. (a) Original image from MIAS database. (b) Contrast-enhanced mammogram using local entropy maximization. (c) Process of pectoral muscle removal. (d) Pectoral muscle removed mammogram.
Figure 4
Figure 4
FP reduction by thresholding (a) clustered image, (b) clusters boundaries marked on original image, and (c) clusters after thresholding.
Figure 5
Figure 5
Variable sizes ROIs from MIAS, DDSM, and TMCH datasets.
Figure 6
Figure 6
Lookup table approach for LBP computation from shape of mass in ROI.
Figure 7
Figure 7
Process for computation of LBP descriptor from shape of mass in ROI. (a) Original image, (b) 3 × 3 window for selection of foreground pixels, (c) lookup table, (d) decision making process, (e) LBP computation from selected foreground pixels.
Figure 8
Figure 8
LBP code computation using sparse curvelet subband coefficients.
Figure 9
Figure 9
(a) FP reduction by clusters marked on original image, (b) FP reduction by thresholding, (c) FP reduction by sparse curvelet coefficient-based LBP, and ANN.
Figure 10
Figure 10
Average classification rate for TMCH dataset.
Figure 11
Figure 11
Average classification rate for MIAS and DDSM dataset.
Figure 12
Figure 12
Average classification rate for MIAS and DDSM dataset.
Figure 13
Figure 13
Average classification rate for MIAS and DDSM dataset.
Figure 14
Figure 14
Representation of fully automatic CAD system for breast cancer using (a) sample mammograms from MIAS, DDSM, and TMCH datasets, (b) preprocessed mammograms, (c) clustered image, (d) TP and FP marked on mammogram, (e) TP marked by thresholding, (f) TP marked by using LBP descriptor based on sparse curvelet coefficients.
Algorithm 1
Algorithm 1
Image fusion for contrast enhancement.
Algorithm 2
Algorithm 2
Algorithm for LBP feature computation based on shape of mass in ROI as.
Algorithm 3
Algorithm 3
Summary of proposed method for FP reduction in mammograms.
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