Mammogram image enhancement and segmentation

Enhancement of mammogram images against low-contrast and poor illumination is still a challenge for researchers. Focusing on such issues, this manuscript presents a two-stage enhancement technique for mammogram images. The first stage of the image enhancement deals with illumination control using the corrective-adaptive gamma correction (CAGC) approach. In the second stage, contrast enhancement operation on the luminosity-controlled image is incorporated. In order to enhance visual perception against low-contrast, the combined application of discrete wavelet transform (DWT) and singular value decomposition (SVD) is incorporated. The experimentation of the proposed technique was performed over a publicly available mini-MIAS dataset. The proposed technique is evaluated on various quantitative parameters such as Pearson correlation coefficient (PCC), universal image quality index (IQI), structural similarity index measurement (SSIM), contrast improvement index (CII), average mean brightness error (AMBE), and mean absolute error (MAE) and obtain the average values of 0.996, 0.912, 0.921, 1.098, 15.732, and 15.624 that are promising results as compared to the other traditional methods. This study also compares the proposed technique with state-of-art methods and achieves better performance, resulting in significant improvement in contrast enhancement and local information preservation of mammogram images.

This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values ​​of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

Logarithmic transformation and histogram equalization (HE) are well-known image enhancement techniques in spatial domain. These techniques are very popular for contrast enhancement because the methods are simple and effective. The basic idea of HE is to remap the gray levels of an image. However, HE tends to introduce some annoying artifacts and unnatural enhancement. Here, we propose an integration of the two techniques at pixel level, doing first the HE and then using logarithmic transformation for mapping purpose. Both the global HE and local HE are preformed on the input image pixels. The histogram equalization has been performed in the MATLAB environment. We have experimented the proposed techniques over a number of sample images and found to produce much better results compared to image enhancement using the inbuilt MATLAB function histeq.

Color image enhancement refers to improving the visual quality of color images by adjusting various attributes such as brightness, contrast, sharpness, and color balance. This enhancement aims to make the images more visually appealing, precise, and easier to interpret, often by accentuating essential features or details while minimizing noise or distortion. Techniques vary from simple adjustments of brightness and contrast to sophisticated algorithms. Effective image enhancement is crucial in numerous fields like medical imaging; enhancing the medical image is vital for accurate diagnosis and treatment planning, as well as remote sensing for improved satellite and aerial imagery. In security and surveillance, enhanced clarity in footage from surveillance cameras, especially in low-light scenarios, is crucial for identifying subjects and activities. However, image enhancement faces several challenges, like noise amplification and Over-enhancement, leading to unnatural-looking images with exaggerated or distorted features. In this paper, low-exposed or night color images are considered for enhancement, and this paper introduces exposure-based recursive histogram equalization techniques along with an Energy Curve instead of the conventional histogram; the energy curve is similar to a histogram based on spatial Contextual Information of an image. The proposed methods are Recursive Exposure-based Sub-image Histogram Equalization, Recursively Separated Exposure-based Sub-image Histogram Equalization, and Exposure-based Sub-image Histogram Equalization techniques considering spatial contextual information of images using an Energy Curve to improve results. These methods were tested on several publicly available datasetswith low-contrast color images and compared with HE, BBHE, DSHE, CLAHE, ESIHE, R-ESIHE, and RS-ESIHE. The proposed techniques are evaluated using parameterslikeAMBE, PSNR, MSE, Entropy, SSIM, and FSIM. The average PSNR values for eight images (taken from TeV DICeneter https://tev.fbk.eu/resources/imageenhancement) using the techniques mentioned above were 9.10817, 20.80568, 12.82645, 10.39347, 16.27458, 15.19979, and 14.59595, respectively. In contrast, the proposed ESIHE with Energy Curve achieved a PSNR of 22.31585, outperforming other methods across multiple metrics, particularly excelling in noise reduction (PSNR), error minimization (MSE: 193.44), structural similarity (SSIM: 0.733319), and feature retention (FSIM: 0.917678). The comparison demonstrates that enhancement methods can be significantly improved by considering spatial contextual information.

Early detection of breast cancer in the mammograms is very essential in the field of medicine. Contrast enhancement for the detection of micro calcification of mammograms based on the Histogram Modified Contrast Limited Adaptive Histogram Equalization (HM-CLAHE) is presented. Histogram equalization is an effective and simple technique for contrast enhancement. The standard histogram equalization (HE) usually results in excessive contrast enhancement because of lack of control on the level of enhancement. The Histogram Modified Contrast Limited Adaptive Histogram Equalization (HM CLAHE) is proposed in this paper to adjust the level of contrast enhancement, which in turn gives the resultant image a strong contrast and brings the local details for more relevant interpretation. It incorporates both histogram modifications as an optimization technique and Contrast Limited Adaptive Histogram Equalization. This method is tested for Mias mammogram images. The performance of this method is determined using the parameter like Enhancement Measure (EME). From the subjective and quantitative measures it is interesting that this proposed technique provides better contrast enhancement with preserving the local information of the mammogram images.

In this paper, Metaheuristic Algorithms such as Genetic Algorithm (GA) and Parallel Ant Colony Optimization (ACO) are implemented to extract the suspicious region based on the asymmetry approach. In bilateral subtraction, the asymmetries between corresponding left and right breast images are considered for extracting the suspicious region from the background tissue. The breast border and the nipple position are used as reference points for alignment of mammograms. The Genetic Operators such as reproduction crossover and mutations are used to detect breast border and using a novel method called Parallel Ant Colony Optimization algorithm identifies the nipple position. The mammogram images are aligned with the help of border points and nipple position and the suspicious regions are extracted by subtracting the left and the right breast image.

The problem of improving the efficiency of contrast enhancement for complex low-contrast images in an automatic mode with an acceptable level of computational costs is considered. A new approach to image enhancement by parameter-free intensity transformation based on an estimate of the bivariate distribution of brightness is proposed. Analysis of the bivariate distribution of brightness enables to significantly improve the efficiency of image contrast enhancement. Based on this approach, a new generalized description for the procedure of intensity transformation based on the analysis of the marginal cumulative distribution function of image brightness is proposed. The relationship between the proposed generalized description of the intensity transformation and the traditional definition of histogram equalization is shown. Based on proposed description, a new technique of modified histogram equalization is proposed. The research confirms the effectiveness of the proposed technique. The proposed technique of modified histogram equalization provides an increase in the efficiency of contrast enhancement compared to the well-known traditional technique of histogram equalization.

Digital image processing is versatile research in this era. Many researchers implement different types of organizations like image restoration, image enhancement, color image processing, image segmentation etc. Image enhancement technique is among the simplest and most appealing area of digital image processing. Enhancement techniques like brightness preservation, contrast enhancement highlight certain features means depend which part of the image want to be enhance some application some input image including noise, reduction or removal of noise is also form of image enhancement. Brightness preservation has enhanced visual quality of digital image so that the limitation contained in these images is used for various applications in a better way. A very popular technique for image enhancement is histogram equalization (HE) and curvelet transformation. HE technique is commonly employed for image enhancement because of its simplicity and comparatively better performance on almost all types of images. Another widely used technique is curvelet transformation. This technique is identified and separate bright regions of image but more error rate and low peak signal to noise ratio(PSNR), result of this technique is brightness preservation level is low and output image is gray. This paper design a hybrid model through discrete cosine transformation, discrete wavelet transformation and combine output of both techniques with image fusion. Proposed algorithm enhanced features and removal noise by decomposition of image using DWT and discrete cosine transformation, adaptive histogram equalization is very important part in this algorithm for smooth image. The tested results of different images are comparing with previous method, generating result with different parameters; less mean square error and high PSNR for improve the quality of an image. This paper presents a hybrid model used various parameter for enhance images like satellite images, medical images etc.

Breast cancer can cause severe side effects on women's health. According to some recent study it has become the second leading cause of woman's death by cancer. Screening is one of the most successful ways of early-stage detection of breast cancer even before it has started showing any symptom. Among other popular screening tests, mammography plays a significant role in early-stage breast cancer detection. Microcalcification is an important characteristic of a mammogram. However, the identification of calcification in the presence of dense breast tissues is a difficult task, contrast enhancement plays a significant role to improve the visual quality of mammograms, thereby helps to identify microcalcifications. In this regard to deal with the challenges of identifying microcalcification from low contrast mammogram images we have applied a two-threshold shadowed set where cardinality has a gradual representation. The overall process is done in two-steps. Separately, we perform the global processing and local processing on the image then finally we combine the effect of both. Furthermore, subjective and objective measures have also been provided along with a comparative study with the stand alone enhancement approaches as part of experimental analysis.

Image Enhancement is a method that converts a poor quality image into a image of superior quality. This paper basically intends to enhance the quality of a image using the Histogram Equalization technique on MATLAB (R2014a) software and the outcome of the image parameters before and after applying Histogram Equalization are discussed. The statistical parameters of the image histogram such as mean and median can be used analyse and show an image of improvised image quality. The method is used to improve the image quality of an X-Ray image of a human bone. This paper also discusses the modern and enhanced techniques of Histogram Equalization and their merits over the traditional Histogram Equalization technique.

In this work, an attempt has been made to identify lesion in mammogram images using edge detection and segmentation method. The edge detection was carried out using inverse maximum likelihood ratio with pre-processing. The pre-processing includes removing of artefacts using top-hat transform and image enhancement using wavelet transform. The lesion edges are identified using inverse maximum likelihood ratio, and a binary mask is generated using active contour to segment the identified lesion. The generated mask is multiplied with the edge detected image to get the final segmented output. The performance of the segmentation method is analysed with ground truth and quantitative performance assessment is carried out using similarity measures based on region overlap and region statistics. These studies appear to be clinically relevant because automated analyses of lesion are important for breast cancer interventions.

This paper demonstrates the significance of histogram processing of an image particu larly the histogram equalization (HE). It is one of the widely used image enhancement technique. It has become a popular technique for contrast enhancement because the method is simple and effect ive. The basic idea of HE is to re-map the gray levels of an image. Here we propose two different techniques of Histogram Equalizat ion namely, the global HE and local HE. The Histogram Equalization has been performed in the MATLA B environ ment. The merits and demerits of both techniques of Histogram Equalization have also been discussed. It is seen after exhaustive experimentation on a nu mber of sample images that the proposed image enhancement techniques can be considered as an imp rovement over the inbuilt MATLAB function histeq.

This study aims to detect the presence of chicken egg embryos using digital image processing. The detection of chicken egg embryos was processed by image segmentation using the thresholding method. The implementation of this method needs to be supplemented with preprocessing phase using methods such as cropping, grayscaling, intensity adjustment, and histogram equalization (HE). The critical emphasis in this research is on image enhancement using a hybrid of Contrast Limited Additive Histogram Equalization (CLAHE) and HE. Based on comparing CLAHE, HE, hybrid CLAHE-HE, or HE-CLAHE, the optimal result used as input for the segmentation process is hybrid CLAHE-HE. The segmentation results using the thresholding method and hybrid CLAHE-HE showed clear and characteristic pictures of chicken egg embryos incomplete detection. Other methods, such as Otsu and k-means, also showed clear and characteristic pictures of chicken egg embryos. However, in terms of processing speed, the thresholding method was faster than the Otsu and k-means methods. The speed comparisons are thresholding<Otsu<k-means (0.57; 0.72; and between 10-10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> seconds). These experiments show that our proposed method (thresholding and hybrid CLAHE-HE) can divide/segment embryonic eggs with significant embryonic characteristics and patterns.

Over the past years many researchers proposed biomedical imaging methods for computer-aided detection and classification of suspicious regions in mammograms. Mammogram interpretation is performed by radiologists by visual inspection. The large volume of mammograms to be analyzed makes such readings labour intensive and often inaccurate. For this purpose, in this paper we propose a new unsupervised method to automatically detect suspicious regions in mammogram images. The method consists mainly of two steps: preprocessing; feature extraction and selection. Preprocessing steps allow to separate background region from the breast profile region. In greater detail, gray levels mapping transform and histogram specifications are used to enhance the visual representation of mammogram details. Then, local keypoints and descriptors such as SURF have been extracted in breast profile region. The extracted keypoints are filtered by proper parameters tuning to detect suspicious regions. The results, in terms of sensitivity and confidence interval are very encouraging.

This paper presents a comparative analysis of fundus image enhancement techniques to detect diabetic retinopathy (DR). Medical images frequently suffers from non-uniform illumination, poor contrast and noise, thus these images have to go through pre-processing stage. For image enhancement many techniques are proposed based on spatial domain (histogram). However, these methods usually decline to produce suitable consequences for non-uniform illumination and wide-ranging of low-contract. The comparative analysis and performance evaluation of various enhancement techniques will help in choosing most suitable technique which may significantly improve the detection of diabetic retinopathy. In this paper histogram equalization (HE), adaptive histogram equalization (ADHE), contrast limited adaptive histogram equalization (CLAHE) and exposure based sub-image histogram equalization (ESIHE) techniques are compared for pre-processing of fundus image for detecting DR. For fair analysis of these techniques, histogram, SNR, entropy, absolute mean brightness error and peak signal-to-noise ratio (PSNR) of fundus images are analyzed by using MATLAB.