Local entropy maximization based image fusion for contrast enhancement of mammogram

Pawar, Meenakshi M.; Talbar, Sanjay N.

doi:10.1016/j.jksuci.2018.02.008

Cited by 18 publications

(10 citation statements)

References 37 publications

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“…The recent work of Pawar et. al in 2021 [22] proposed local entropy maximization based image fusion for mammogram enhancement. They used Edge Contents(EC) and FSIM (Feature Similarity Index Measure) for comparing the performance of the method.…”

Section: Resultsmentioning

confidence: 99%

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Enhancement of Screening Mammograms for Better Mass Inspection using Dual-Tree Complex Wavelet Transform

CHITTATTU

Naz

Narayanan

2022

Preprint

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Breast cancer has become one of the most common cancers worldwide. It is also one of the leading causes of death in women. Early detection of breast cancer is the need of the hour as it offers increased treatment options, better survival chances and improved quality of life. The most economic technique is screening mammograms, to promote breast cancer identification at an early stage. However, image quality of screening mammograms is poor because they use low dose X-rays for image acquisition. Hence special attention is to be paid for enhancing the quality of screening mammograms.This paper proposes an enhancement strategy for screening mammograms based on Dual-Tree Complex Wavelet Transform (DTCWT).The DTCWT yields six directionally selective high pass sub-bands at +150,-150,+450,-450,+750,-750 on multiple scales.The reconstruction from the decomposed bands produce resultant image with enhancement in directional edges. Thus edge details are enhanced, which provides improved visualization of mass boundaries present inside the breast tissues.The experimental study on three publicly available mammogram datasets using the proposed DTCWT has the lowest Absolute Mean Brightness Error (AMBE) and Root Mean square Error (RMSE). Also, this method has higher values for Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR), which are indicators of overall image quality improvement and preservation of structural similarity. The proposed method offers PSNR values of 54.3612, 46.043, 48.8719 and SSIM is 0.9977, 0.9869, 0.9916 for the MIAS, DDSM and Inbreast datasets respectively.The proposed DTCWT based mammogram enhancement can be used as an efficient preprocessing technique in a computer-aided diagnosis system to detect breast cancer in its early stage. The comparison of the proposed method with many of the recent algorithms depicts the overall dominance of its performance, with the minimum error and good signal enhancement.

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Section: Resultsmentioning

confidence: 99%

“…AMBE [22]is the measure of error between the values of pixel intensities of the original and enhanced images and is calculated as in equation (11).…”

Section: Absolute Mean Brightness Error (Ambe)mentioning

confidence: 99%

Enhancement of Screening Mammograms for Better Mass Inspection using Dual-Tree Complex Wavelet Transform

CHITTATTU

Naz

Narayanan

2022

Preprint

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“…In literature, the authors proposed a variety of contrast enhancement techniques such as Histogram Equalization (HE) [27], Median Filtering [28], filtering with morphological operators and un-sharp masking [29] to improve the visual contents of mammograms [30]. In [31] the authors used Local Contrast Enhancement (LCE) to enhance the contrast in images and achieved better results.…”

Section: Contrast Limited Advanced Histogram Equalizationmentioning

confidence: 99%

A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach

Kumari¹,

Jagadesh²

2022

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Breast cancer is the most hazardous disease among women worldwide. A simple, cost-effective, and efficient screening called mammographic imaging is used to find the breast abnormalities to detect breast cancer in the early stages so that the patient's health can be improved. OBJECTIVES: The main challenge is to extract the features by using a novel technique called Advanced Gray-Level Cooccurrence Matrix (AGLCM) from pre-processed images and to classify the images using machine learning algorithms. METHODS: To achieve this, we proposed a four-step process: image acquisition, pre-processing, feature extraction, and classification. Initially, a pre-processing technique called Contrast Limited Advanced Histogram Equalization (CLAHE) is used to increase the contrast of images and the features are retrieved using AGLCM which extracts texture, intensity and shape-based features as these are important to identify the abnormality. RESULTS: In our framework, a classifier called eXtreme Gradient Boosting (XGBoost) is applied on mammograms and the results are compared with other classifiers such as Random Forest (RF), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The experiments are done on the Mammographic Image Analysis Society (MIAS) dataset. CONCLUSION: The outcome achieved with CLAHE+ AGLCM+ XGBoost classifier is better than the existing methods.In future, we experiment on large datasets and also concentrate on optimal features selection to increase the classification.

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“…IHFE computes the comparative divergence value among different ambiguous pixels information in the intuitionistic fuzzy image. 9,34,35 The IHFE (Ent) of a given colonogram I X parameterized by λ is formulated as 9,34,35 :…”

Section: 37mentioning

confidence: 99%

“…Various contrast enhancement methods have been developed in the last few decades in both spatial domain 6,7 and transform domain. 8,9 A series of recent studies for contrast enhancement such as histogram equalization (HE), [10][11][12] bi-histogram equalization (BHE), 12,13 recursive mean-separate histogram equalization (RMSHE), [10][11][12] and an adaptive gamma correction with weighting distribution (AGCWD) 12,13 have been discussed in literature. It has been suggested by Wang et al that an adaptive image enhancement algorithm is used for correcting low-illumination effect using nonlinear functional transformation.…”

Section: Introductionmentioning

confidence: 99%

A novel intuitionistic fuzzy soft set based colonogram enhancement for polyps localization

Ghosh

Ghosh²

2021

Int J Imaging Syst Tech

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This article represents a colonogram enhancement approach using intuitionistic fuzzy set (IFS) and fuzzy soft set (FSS) to improve the visual quality and highlight the local details in enhanced images without artifacts. The proposed method has the advantages of IFSs and fuzzy soft sets (FSSs) which can deal with gray-level ambiguity in colonoscopy images. The combination of IFS and FSS works on intricate color variations with an adaptive parametric maps. First, the S-membership function has been applied on the images to map into intuitionistic fuzzy space. We obtain intuitionistic fuzzy image from source image followed by decomposition of several blocks. Thereafter, the FSS is employed to get subsequent intuitionistic fuzzy soft matrix from individual image block. The FSS utilizes a class of parametric coefficients to obtain the hesitant score of individual pixels in each block through the soft-score measure (SSM). Finally, the proposed method intensifies each membership degree in all blocks in the fuzzy plane using SSM and achieves an enhanced colonogram via defuzzification. Extensive experiment involving large data sets shows that the designed method exhibits better performance in contrast enhancement and visual quality improvement of colonograms for malformation detection (such as a polyps, malignant precancerous cells) in comparison with the stateof-art methods.

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Local entropy maximization based image fusion for contrast enhancement of mammogram

Cited by 18 publications

References 37 publications

Enhancement of Screening Mammograms for Better Mass Inspection using Dual-Tree Complex Wavelet Transform

Enhancement of Screening Mammograms for Better Mass Inspection using Dual-Tree Complex Wavelet Transform

A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach

A novel intuitionistic fuzzy soft set based colonogram enhancement for polyps localization

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