Histogram-modified local contrast enhancement for mammogram images

Sundaram, M.; Ramar, K.; Arumugam, N.; Prabin, G.

doi:10.1504/ijbet.2012.047371

Cited by 39 publications

(42 citation statements)

References 19 publications

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“…The algorithm's performance is evaluated by 4 global image enhancement evaluation methods: the EME, AMBE, PSNR [16][17][18], and CIR [5].…”

Section: Resultsmentioning

confidence: 99%

“…The ABC algorithm is used for finding the optimum values of these coefficients in this study. While finding the optimum values, the universal image quality index (UIQI) [16][17][18] value is used in the fitness function.…”

Section: Methodsmentioning

confidence: 99%

“…A fitness function for evaluating the adequacy of the coefficients is defined. The UIQI is used in the fitness function [16][17][18]. The UIQI function is a general image enhancement measuring function defined as:…”

Section: Generating the Linear Combination Coefficient Setsmentioning

confidence: 99%

See 2 more Smart Citations

Contrast enhancement using linear image combinations algorithm (CEULICA) for enhancing brain magnetic resonance images

Yılmaz¹,

Özbay²

2014

Turk J Elec Eng & Comp Sci

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Brain magnetic resonance imaging (MRI) images support important information about brain diseases for physicians. Morphological alterations in brain tissues indicate the probable existence of a disease in many cases. Proper estimation of these tissues, measuring their sizes, and analyzing their image patterns are parts of the diagnosis process.Therefore, the interpretability and perceptibility level of the MRI image is valuable for physicians. In this paper, a new image contrast enhancement algorithm based on linear combinations is presented. The proposed algorithm is focused on improving the interpretability and perceptibility of the image information. An MRI image is presented to the algorithm, which generates a set of images from this MRI image. After this step, the algorithm uses the linear combination technique for combining the image set to generate a final image. Linear combination coefficients are generated using the artificial bee colony algorithm. The algorithm is evaluated by 4 different global image enhancement evaluation techniques: contrast improvement ratio (CIR), enhancement measurement error (EME), absolute mean brightness error (AMBE), and peak-signal-to-noise ratio (PSNR). During the evaluation process, 2 case studies are performed. The first case study is performed with 3 different image sets (T1, T2, and proton density) presented to the algorithm. These sets are obtained from the Brainweb simulated MRI database. The algorithm shows the best performance on the T1 image set with 5.844 CIR, 6.217 EME, 15.045 AMBE, and 22.150 dB PSNR scores as average values. The second case study is also performed with 3 different image sets (T1-fast low-angle shot sequence, T1-magnetization-prepared rapid acquired gradient-echoes (MP-RAGE), and T2) obtained from the The Multimedia Digital Archiving System public community database. The algorithm performs best with the T1-MP-RAGE modality images with 6.983 CIR, 17.326 EME, 3.514 AMBE, and 30.157 dB PSNR scores as average values. In addition, this algorithm can be used for classification tasks with proper linear combination coefficients, for instance, segmentation of the white matter regions in brain MRI images.

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“…The algorithm's performance is evaluated by 4 global image enhancement evaluation methods: the EME, AMBE, PSNR [16][17][18], and CIR [5].…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Contrast enhancement using linear image combinations algorithm (CEULICA) for enhancing brain magnetic resonance images

Yılmaz¹,

Özbay²

2014

Turk J Elec Eng & Comp Sci

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“…Disini koefisien NSCT dimanipulasi dengan koefisien non linier. Penelitian lainnya yaitu oleh M. Sundaram, dkk [5], melakukan perbandingan terhadap berbagai macam teknik perbaikan kontras di antaranya histogram equalization (HE), CLAHE, Unsharp masking filter dan histogram modified local contrast enhancement (HMLCE). Dari penelitiannya disimpulkan bahwa CLAHE merupakan metode yang baik untuk perbaikan kontras.…”

Section: Pendahuluanunclassified

Perbandingan Metode Untuk Perbaikan Kualitas Citra Mammogram

Fajrin

2016

Simet

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ABSTRAKKanker payudara merupakan pembunuh nomor satu pada wanita di seluruh dunia. Sejauh ini, deteksi dari kanker payudara hanya menggunakan mata telanjang dan berdasarkan jam terbang (pengalaman) dari dokter dan radiologis. Terdapat beberapa kelemahan dalam menganalisis mammogram payudara guna mendeteksi keberadaan kanker payudara. Hal ini bisa diakibatkan oleh sel kanker yang tertutup oleh noise, kontras citra yang rendah dan faktor manusiawi lainnya seperti : kelelahan, mood, dan lainnya. Untuk meminimalisir hal tersebut dibutuhkan suatu metode yang dapat membantu dokter dalam menganalisis citra mammogram payudara. Pada penelitian ini, dilakukan suatu proses yang bertujuan untuk meningkatkan kualitas mammogram agar lebih memudahkan dokter dalam mendiagnosis kelainan pada payudara. Citra yang diolah merupakan citra mammogram yang tidak dipangkas dan tidak disegmentasi pada bagian Region of Interest (ROI), melainkan keseluruhan citra payudara setelah dihilangkan background yang berlebihan. Tahapan pada proses peningkatan kuliatas citra mammogram ini (pra pengolahan) terdiri dari : menghilangkan label atau artifak yang ditemukan pada mammogram, meng-crop citra pada bagian payudara saja (menghilangkan background), memperbaiki kontras citra dengan membandingkan beberapa metode yaitu: CLAHE, Non Subsampled Contourlet Transform (NSCT), Contras Stretching (CS) dan selanjutnya dilakukan smoothing dengan menggunakan filter median. Kinerja dari setiap metode dihitung dengan mencari nilai Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR) citra. Dari nilai MSE dan PSNR yang didapatkan, ditemukan nilai MSE dan PSNR terbaik pada metode NSCT dengan nilai 50.20878 db 31.75332 db. Kata kunci: CLAHE, NSCT, CS, median filter. ABSTRACT

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“…They broke down the adequacy of their versatile neighborhood differentiate upgrade (ANCE) method in expanding the affectability of bosom malignancy determination. An option method for mammogram differentiate upgrade utilizing wavelet based techniques came about as a part of change in saving the points of interest in the picture at the cost of clamor enhancement [10]. …”

Section: Introductionmentioning

confidence: 99%

Optimization of Features for Image Enhancement Using Local and Global Features

2017

IJSR

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Image enhancement is used to improve the digital quality of image. It is used to improve the poor quality of image that is too used to improve bad quality of picture into good picture or image. Contrast enhancement is acquiring clear image through brightness intensity value redistribution. For the enhancement of image used feature optimization technique. The feature optimization technique used particle of swarm optimization for the selection of local and global feature of image. The optimized feature is distributing overall image and the quality of image enhanced. The experimental results show the mean with the traditional enhancement methods, the proposed threshold-based enhancement digital image enhancement algorithm for mixed digital image enhancement is relatively clear, especially in the more damage pixel, more complex cases", can show its good performance.

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Histogram-modified local contrast enhancement for mammogram images

Cited by 39 publications

References 19 publications

Contrast enhancement using linear image combinations algorithm (CEULICA) for enhancing brain magnetic resonance images

Contrast enhancement using linear image combinations algorithm (CEULICA) for enhancing brain magnetic resonance images

Perbandingan Metode Untuk Perbaikan Kualitas Citra Mammogram

Optimization of Features for Image Enhancement Using Local and Global Features

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