DIGITAL MAMMOGRAM ENHANCEMENT USING KAPUR MEASURE OF ENTROPY AND MATHEMATICAL MORPHOLOGY

2013 ◽  
Vol 25 (03) ◽  
pp. 1350029 ◽  
Author(s):  
Baljit Singh Khehra ◽  
Amar Partap Singh Pharwaha
Keyword(s):  
Breast Cancer ◽  
Image Analysis ◽  
Early Detection ◽  
Contrast Enhancement ◽  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Mammogram Image ◽  
Background Contrast

Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzified using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzified mammogram. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a nonuniform background. Main advantage of Kapur measure of entropy over Shannon entropy is that Kapur measure of entropy has α and β parameters that can be used as adjustable values. These parameters can play an important role as tuning parameters in the image processing chain for the same class of images. Experiments have been conducted on images of mini-Mammogram Image Analysis Society (MIAS) database (UK). Experiment results of the proposed approach are compared with histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE) and fuzzy histogram hyperbolization (FHH) which are well-established image enhancement techniques. In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and dense-glandular) of mini-MIAS database are considered. Objective image quality assessment parameters: Target-to-background contrast enhancement measurement based on standard deviation (TBCSD), target-to-background contrast enhancement measurement based on entropy (TBCE), contrast improvement index (CII), peak signal-to-noise ratio (PSNR) and average signal-to-noise ratio (ASNR) are used to evaluate the performance of proposed approach. The experimental results show that the proposed approach performs well. This study can be a part of developing a computer-aided diagnosis (CAD) system for early detection of breast cancer.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

scholarly journals Perbandingan Metode Contrast Enhancement pada Citra CT-Scan Kanker Paru-paru

Gravitasi ◽  
2020 ◽  
Vol 19 (2) ◽  
pp. 24-28
Author(s):  
Nurhidayah ◽  
Bannu Abdul Samad ◽  
Bualkar Abdullah
Keyword(s):  
Ct Scan ◽  
Contrast Enhancement ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Histogram Equalization ◽  
Mean Square ◽  
Signal To Noise ◽  
Adaptive Histogram Equalization ◽  
Noise Ratio

Abstrak: Di Indonesia kanker paru menjadi penyebab kematian kedua setelah kanker payudara. Angka mortalitas yang cukup tinggi, maka penentuan diagnosis lebih awal memegang peranan yang sangat penting dalam manajemen terapi. Kelemahan CT-Scan dalam mendiagnosa kanker paru-paru disebabkan oleh kontras citra yang rendah dan derau pada citra. Pada penelitian ini akan membandingkan metode contrast enhancement berbasis histogram equalization dan contrast limited adaptive histogram equalization untuk meningkatkan kualitas citra dengan menggunakan software Matlab. Namun, sebelumnya dilakukan reduksi noise dengan menggunakan metode median filter. Kinerja dari setiap metode dihitung dengan mencari nilai MSE (Mean Square Error) dan PSNR (Peak Signal to Noise Ratio) citra. Dari nilai MSE dan PSNR yang di dapatkan diperoleh nilai MSE dan PSNR terbaik pada metode contrast limited adaptive histogram equalization dengan nilai 653,434 dB dan 245,547 dB.

Entropy based Range Optimized Brightness Preserved Histogram-Equalization for Image Contrast Enhancement

2016 ◽  
Vol 6 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Sankhadip Sen ◽  
Kaustav Sarkar ◽  
Sanjoy Das
Keyword(s):  
Contrast Enhancement ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Image Contrast ◽  
Experimental Results ◽  
Signal To Noise ◽  
Image Histogram ◽  
Image Contrast Enhancement ◽  
Noise Ratio

In this study the over-enhancement problem of traditional Histogram-Equalization (HE) has been removed to some extent by a variant of HE called Range Optimized Entropy based Bi-Histogram Equalization (ROEBHE). In ROEBHE image histogram has been thresholded into two sub-histograms i.e. histograms corresponding to background and foreground. The threshold is calculated by maximizing the sum of the entropy of these two sub-histograms. The range for equalization has been optimized by maximizing the Peak-Signal to Noise ratio (PSNR). The experimental results prove that ROEBHE has prevailed over existing methods and PSNR is a better range optimizer than Absolute Mean Brightness Error (AMBE).

scholarly journals Increased Mammogram Image Contrast Using Histogram Equalization And Gaussian In The Classification Of Breast Cancer

2020 ◽  
Vol 4 (01) ◽  
pp. 40-44
Author(s):  
Febri Liantoni ◽  
Coana Sukmagautama ◽  
Risalina Myrtha
Keyword(s):  
Breast Cancer ◽  
Contrast Enhancement ◽  
Nearest Neighbor ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
Image Contrast ◽  
K Nearest Neighbor ◽  
Diagnose Breast Cancer ◽  
Image Contrast Enhancement ◽  
Mammogram Image

Breast cancer is one of the most common diseases among women in several countries. One of the most common methods to diagnose breast cancer is mammography. In this study, we propose a classification study to differentiate benign and malignant breast tumors based on mammogram image. The proposed system includes five major steps, i.e. preprocessing, histogram equalization, convolution, feature extraction, and classification. Image is cropped using region of interest (ROI) at preprocessing stage. In this study, we perform image contrast quality enhancement of the mammogram to view the breast cancer better. Image contrast enhancement uses histogram equalization and Gaussian filter. Gray-Level Co-Occurrence Matrix (GLCM) is used to extract the mammogram features. There are five features used i.e. entropy, correlation, contrast, homogeneity, and variance. The last step is to classify using naïve Bayes classifier (NBC) and k-nearest neighbor (KNN). Based on the hypothesis, the accuracy of NBC method is 90% and the accuracy of KKN method is 87.5%. So, the mammogram image contrast enhancement is well performed.

scholarly journals Image Enhancement based on Contrast Enhancement & Fuzzification Histogram Equalization and Comparison with Contrast Enhancement Techniques

2013 ◽  
Vol 7 (2) ◽  
pp. 594-599
Author(s):  
Shubhanshi Gupta ◽  
Ashutosh Gupta ◽  
Gagan Minocha
Keyword(s):  
Fuzzy Logic ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Adaptive Histogram Equalization ◽  
Image Contrast Enhancement

Contrast Enhancement is a technique which comes into the part of Image Enhancement. Contrast Enhancement is used to enhance the visual quality of any captured or other image. Contrast Enhancement can be performed with the help of Histogram equalization (HE). In this technique, the image is collected in the gray scale allocation. The image is then partitioning and applying adaptive Histogram equalization (AHE). Fuzzy logic provides a set of logics which enhance the contrast and visibility of any image. In this technique, the visual quality and the contrast of image will change and then compare these results with previous techniques. The performance of several established image enhancement techniques is presented in terms of different parameters like Absolute mean brightness error (AMBE), Peak signal to noise ratio (PSNR), contrast and Visual quality.

Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

2020 ◽  
Vol 4 (2) ◽  
pp. 53-60
Author(s):  
Latifah Listyalina ◽  
Yudianingsih Yudianingsih ◽  
Dhimas Arief Dharmawan
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Image Quality ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Vital Role ◽  
Retinal Images ◽  
Signal To Noise ◽  
Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

The Nth‐root stack: Theory, applications, and examples

Geophysics ◽  
1986 ◽  
Vol 51 (10) ◽  
pp. 1879-1892 ◽  
Author(s):  
P. L. McFadden ◽  
B. J. Drummond ◽  
S. Kravis
Keyword(s):  
Contrast Enhancement ◽  
Signal To Noise Ratio ◽  
Beam Steering ◽  
Seismic Refraction ◽  
Signal Distortion ◽  
Signal Attenuation ◽  
Signal To Noise ◽  
Zero Crossing ◽  
Array Data ◽  
Noise Ratio

Multichannel geophysical data are usually stacked by calculating the average of the observations on all channels. In the Nth‐root stack, the average of the Nth root of each observation is raised to the Nth power, with the signs of the observations and average maintained. When N = 1, the process is identical to conventional linear stacking or averaging. Nth‐root stacking has been applied in the processing of seismic refraction and teleseismic array data. In some experiments and certain applications it is inferior to linear stacking, but in others it is superior. Although the variance for an Nth‐root stack is typically less than for a linear stack, the mean square error is larger, because of signal attenuation. The fractional amount by which the signal is attenuated depends in a complicated way on the number of data channels, the order (N) of the stack, the signal‐to‐noise ratio, and the noise distribution. Because the signal‐to‐noise ratio varies across a wavelet, peaking where the signal is greatest and approaching zero at the zero‐crossing points, the attenuation of the signal varies across a wavelet, thereby producing signal distortion. The main visual effect of the distortion is a sharpening of the legs of the wavelet. However, the attenuation of the signal is accompanied by a much greater attenuation of the background noise, leading to a significant contrast enhancement. It is this sharpening of the signal, accompanied by the contrast enhancement, that makes the technique powerful in beam‐steering applications of array data. For large values of N, the attenuation of the signal with low signal‐to‐noise ratios ultimately leads to its destruction. Nth‐root stacking is therefore particularly powerful in applications where signal sharpening and contrast enhancement are important but signal distortion is not.

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