Unified Preprocessing and Enhancement Technique for Mammogram Images

Tripathy, Sushreeta; Swarnkar, Tripti

doi:10.1016/j.procs.2020.03.223

Cited by 39 publications

(11 citation statements)

References 10 publications

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“…After that, fractal methods are applied to extract asymmetric information from lesions to detect abnormal lesions. Tripathy et al [14] used the Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement and thresholding methods to detect the boundaries of breast tumors from the digital mammogram.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

Soliman¹,

Ali²,

Aly³

et al. 2022

Computers, Materials &Amp; Continua

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Breast cancer is the most common type of cancer, and it is the reason for cancer death toll in women in recent years. Early diagnosis is essential to handle breast cancer patients for treatment at the right time. Screening with mammography is the preferred examination for breast cancer, as it is available worldwide and inexpensive. Computer-Aided Detection (CAD) systems are used to analyze medical images to detect breast cancer, early. The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations. Processing of mammogram images has four main steps: pre-processing, segmentation of the region of interest, feature extraction and classification of the images into normal or abnormal classes. This paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect masses. The pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter, removal of artifacts using morphological operations, and contrast enhancement using a fuzzy enhancement technique. The proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure (EME) and local contrast metrics. The comparison shows an outstanding performance of the proposed technique from the visual and numerical perspectives. The segmentation process is performed using Otsu's multiple thresholding method. This method segments the image regions into five classes with variable intensities using four thresholds. Its effectiveness is measured based on visual quality of the segmentation output, as it gives details about the image and positions of masses. The performance of the proposed framework is measured using Dice coefficient, Hausdorff, and Peak Signal-to-Noise Ratio (PSNR) metrics. The segmented tumor region with the proposed segmentation method is 81% of the ground truth region provided

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Section: Related Workmentioning

confidence: 99%

An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

Soliman¹,

Ali²,

Aly³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Rundo et al [25] use genetic algorithms in order to improve the performance of segmentation methods in medical magnetic resonance images. Tripathy et al [26] perform segmentation using a threshold method on mammogram images, after enhancing contrast using the CLAHE algorithm. Some systems that integrate both detection and segmentation stage still require manual rejection of false positives before the segmentation stage, as happens in [27,28].…”

Section: Detection and Segmentation Of Massesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Study on Data Partition for Delimitation of Masses in Mammography

2021

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Mammography is the primary medical imaging method used for routine screening and early detection of breast cancer in women. However, the process of manually inspecting, detecting, and delimiting the tumoral massess in 2D images is a very time-consuming task, subject to human errors due to fatigue. Therefore, integrated computer-aided detection systems have been proposed, based on modern computer vision and machine learning methods. In the present work, mammogram images from the publicly available Inbreast dataset are first converted to pseudo-color and then used to train and test a Mask R-CNN deep neural network. The most common approach is to start with a dataset and split the images into train and test set randomly. However, since there are often two or more images of the same case in the dataset, the way the dataset is split may have an impact on the results. Our experiments show that random partition of the data can produce unreliable training, so the dataset must be split using case-wise partition for more stable results. In experimental results, the method achieves an average true positive rate of 0.936 with 0.063 standard deviation using random partition and 0.908 with 0.002 standard deviation using case-wise partition, showing that case-wise partition must be used for more reliable results.

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“…S. Tripathy et al [19] The study used histogram equalization and CLAHE for the effective analysis of clinical mammograms. The percentage of highly contrasted pixels was evaluated with the suggested system.…”

Section: Related Workmentioning

confidence: 99%

Performance Enhancement of Mammogram Preprocessing for Breast Cancer Detection using Improved Successive Mean Quantization Technique

2020

IJETER

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In the recent years the rise in cancer cases are at the peak and among different cancer sites the breast cancer is leading the race. It stood second most dangerous cancer among all cancer sites and it is a leading type of cancer among women's and also leading cause of cancer deaths among women's now a days. The thorough studies said that the survival rate of breast cancer is quite good but when it is detected early stage. In this paper we have highlighted the different obstacles that might leads the misprediction by the concern due to many types of artifacts and noise present in the mammogram and it is the main reason of misprediction and accuracy degradation. The paper presents the Improved Successive Mean Quantization Techniques (SMQT) with top hat transform, exponentiation and histogram enhancement to improve the quality of image with accurate region of interest and intensity image enhancement will play to vital role. Proposed model will give better accuracy when tested in MIAS and DDSM dataset it gives approximately 93 to 95 percent accuracy in all mammograms and average overlap ratio is grater that 85% in all tested mammograms.

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Unified Preprocessing and Enhancement Technique for Mammogram Images

Cited by 39 publications

References 10 publications

An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

Study on Data Partition for Delimitation of Masses in Mammography

Performance Enhancement of Mammogram Preprocessing for Breast Cancer Detection using Improved Successive Mean Quantization Technique

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