An improved clipped sub-histogram equalization technique using optimized local contrast factor for mammogram image analysis / Nurshafira Hazim Chan
Mammography has been known worldwide as the most common imaging modalities utilized for early detection of breast cancer. The mammographic images produced are in greyscale, however they often produced poor contrast images, non-uniform illumination, and the image often contain artefacts and noise. Th...
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| Format: | Thesis |
| Published: |
2021
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| Online Access: | http://studentsrepo.um.edu.my/13217/1/Nurshafira_Hazim_Chan.jpg http://studentsrepo.um.edu.my/13217/8/nurshafira.pdf http://studentsrepo.um.edu.my/13217/ |
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| Summary: | Mammography has been known worldwide as the most common imaging modalities utilized for early detection of breast cancer. The mammographic images produced are in greyscale, however they often produced poor contrast images, non-uniform illumination, and the image often contain artefacts and noise. These limitations can be overcame during the pre-processing stage by improving the image enhancement process. Therefore, in this research an optimized enhancement framework is developed where the local contrast factor is manipulated to preserve details of the image. This technique aims to improve the overall image visibility without altering histogram of the original image, which will affect the segmentation and classification processes. Unwanted pixel removal is performed in the image histogram at early stage to increase the efficiency of mean histogram calculation. Then, the histogram is separated into two partitions to allow histogram clipping process to be conducted individually for underexposed and overexposed areas. Consequently, the local contrast factor optimization is conducted to preserve the image details. The proposed method is tested on 322 MIAS database images, and the results from the proposed method are compared with other methods such as HE, CLAHE, DPPLHE, BPPLHE, and QPLBHE by the quantitative measurement of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), average contrast (AC), and average entropy (AE) difference. The results portrayed that the proposed method yield better quality over the others with highest peak signal-to-noise ratio of 32.676. In addition, in terms of qualitative analysis, the proposed method depicted better lesion segmentation with smoother shape of the lesion. |
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