Recursive Mean Separate Histogram Equalization Research Articles

Enhancing Detection of Microcalcifications using FADHECAL for Early Stage Breast Cancer

Microcalcifications (MCCs) are reliable early signs of breast cancer. However, the small size of calcifications and low radiation factors used in digital mammograms cause low and poor quality mammogram images in detecting MCCs. This paper presents an image enhancement technique called Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) to enhance the details of MCCs in mammogram images by reducing the image noise while conserving contrast and brightness. A total of 23 mammogram images with MCCs were retrieved from the Mammographic Image Analysis Society’s database. The enhancement performance of FADHECAL was compared with Recursive Mean-Separate Histogram Equalization, Histogram Equalization and Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization. Image quality measurement tools of absolute mean brightness error (AMBE), structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used. The results showed that FADHECAL had the most superior results among other enhancement techniques, with 6.302 of AMBE, 20.453 of PSNR and 0.851 of SSIM. The proposed FADHECAL exhibited a high accuracy of 91.30% for the detection of MCCs. Hence, FADHECAL can be used as an ideal tool for identifying MCCs in early-stage breast cancer.

Using FC-CLAHE-ADF to enhance digital mammograms for detecting breast lesions

Digital mammograms are commonly used for breast screening, especially to aid the detection of cancer. However, digital mammograms suffer with low contrast images due to the low exposure factors used. This paper presents a novel image enhancement technique for digital mammogram images known as Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization with Anisotropic Diffusion Filter (FC-CLAHE-ADF). This proposed FC-CLAHE-ADF has adopted a fuzzy-based and histogram-based image enhancement technique where it can further reduce the noise of the digital mammograms while preserving the contrast and brightness. A total of six digital mammograms were retrieved from Mammographic Image Analysis Society (MIAS) open-source database. The performance of FC-CLAHE-ADF was compared to Recursive Mean-Separate Histogram Equalization (RMSHE), Fast Discrete Curvelet Transform via Unequally Spaced Fast Fourier Transform (FDCT-USFFT), and FC-CLAHE only. In summary, this novel FC-CLAHE-ADF has provided the most superior results, among other selected enhancement techniques. The resulting images have been able to demonstrate breast lesions better.

Automatic Skin Tumour Segmentation Using Prioritized Patch Based Region – A Novel Comparative Technique

Skin tumour detection plays a key factor in medical research. Nowadays, tumour detection process is of crucial importance as the number of persons affected is increasing substantially. The aim of this research work is to develop a new approach for efficient image enhancement and tumour detection from other unaffected regions on computed tomographic skin images. This work is mainly related to medical application methods on computed tomography (CT) skin tumour images that have been designed and implemented efficiently. The initial method, which was based on the quality of image, enhanced the medical image performance. Normally, these images are very noise sensitive and create difficulty in handling procedures. Proper care has to be taken which involves the introduction of pre-processing algorithms like enhancement techniques and filters. According to this, Anisotropic Diffusion Filtering (ADF) followed by Recursive Mean Separate Histogram Equalization (RMSHE) algorithm was introduced to improve the contrast of tumour images. In the second method, Public Contour Metric Based Segmentation (PCMBS) Mapping and Prioritized Patch Based Region Segmentation (PPBRS) Algorithm is proposed for skin tumour segmentation. These techniques are performed in CT skin tumour image which are benign or malignant. Overall accuracy of 98.5% and 95.4% is obtained for various benign and malignant tumours, respectively, in MATLAB 2018a software.

Recursive weighted multi-plateau histogram equalization for image enhancement

Histogram equalization is broadly used for contrast enhancement. However, it does not take brightness preservation into account. It is not suitable for real time applications like medical, synthetics aperture radar (SAR), consumer electronic products, etc. whereas the brightness preservation is of vital importance to avoid the annoying artifacts. This paper proposes a histogram equalization based method called recursive weighted multi-plateau histogram equalization (RWMPHE). The essential idea is to segment the histogram into two or more sub histograms followed by clipping with six plateau limits. Weighting process using normalized power law modifies the clipped histogram to equalize each partition independently using histogram equalization. Recursive mean separate histogram equalization (RMSHE), recursive sub-image histogram equalization (RSIHE) and recursively separated and weighted histogram equalization (RSWHE) are methods that perform similar histogram segmentation but do not carry out clipping with weighting for histogram modification. In this paper, it is shown that RWMPHE preserve the brightness and enhances the contrast more precisely and accurately than existing methods.

A Comparative Study of Contrast Enhancement using Image Fusion

Image enhancement is used to improve the visual quality of an image. In this paper a different approach on image fusion has been described for image enhancement. The two main algorithms involved in this approach are Recursive Mean Separate Histogram Equalization and Linear Image Fusion. To get the fused image in this approach the input images are the original image and the Recursive Mean Separate Histogram Equalised image of the original image. The quality of an image has been judged by the parameters of Average Gradient and Standard Deviation and the result is compared with the image obtained after Linear Image Fusion.

Contrast Enhancement and Brightness Preservation Using Multi-Decomposition Histogram Equalization

Histogram Equalization (HE) has been an essential addition to the Image Enhancement world. Enhancement techniques like Classical Histogram Equalization (CHE), Adaptive Histogram Equalization (ADHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE) methods enhance contrast, however, brightness is not well preserved with these methods, which gives an unpleasant look to the final image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization (MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input sixty-four parts, applied CHE in each of the sub-images and then finally interpolated them in correct order. The final image after MDHE results in contrast enhanced and brightness preserved image compared to all other techniques mentioned above. We have calculated the various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported by bar graphs, histograms and the parameter calculations at the end.

Study of Various Histogram Equalization Techniques

Histogram equalization (HE) works well on single channel images for contrast enhancement. However, the technique used is ineffective on multiple channel images. So, it is not suitable for consumer electronic products, where preserving the original brightness is necessary in order not to introduce unnecessary visual deterioration. Bi-histogram equalization (BHE) has been developed and it is analyzed mathematically.BHE separates the input image's histogram into two, based on its mean before equalizing them independently so that it can preserve the original brightness up to certain extends. Recursive Mean-Separate Histogram Equalization (RMSHE) is another technique to provide better and scalable brightness preservation for gray scale and color images. While the separation is done only once in BHE, RMSHE performs the separation recursively based on their respective mean. It is analyzed mathematically that the output images mean brightness will converge to the input images mean brightness as the number of recursive mean separation increases. The recursive nature of RMSHE also allows scalable brightness preservation, which is very useful in consumer electronics. Finally a comparative study was made to analyze all the above methods using gray scale and color images.

Reducing charging effects in scanning electron microscope images by Rayleigh contrast stretching method (RCS)

To reduce undesirable charging effects in scanning electron microscope images, Rayleigh contrast stretching is developed and employed. First, re-scaling is performed on the input image histograms with Rayleigh algorithm. Then, contrast stretching or contrast adjustment is implemented to improve the images while reducing the contrast charging artifacts. This technique has been compared to some existing histogram equalization (HE) extension techniques: recursive sub-image HE, contrast stretching dynamic HE, multipeak HE and recursive mean separate HE. Other post processing methods, such as wavelet approach, spatial filtering, and exponential contrast stretching, are compared as well. Overall, the proposed method produces better image compensation in reducing charging artifacts.

Survey of Contrast Enhancement Techniques based on Histogram Equalization

This Contrast enhancement is frequently referred to as one of the most important issues in image processing. Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. Histogram equalization (HE) has proved to be a simple and effective image contrast enhancement technique. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. This paper presents a review of new forms of histogram for image contrast enhancement. The major difference among the methods in this family is the criteria used to divide the input histogram. Brightness preserving Bi-Histogram Equalization (BBHE) and Quantized Bi-Histogram Equalization (QBHE) use the average intensity value as their separating point. Dual Sub-Image Histogram Equalization (DSIHE) uses the median intensity value as the separating point. Minimum Mean Brightness Error Bi-HE (MMBEBHE) uses the separating point that produces the smallest Absolute Mean Brightness Error (AMBE). Recursive Mean-Separate Histogram Equalization (RMSHE) is another improvement of BBHE. The Brightness preserving dynamic histogram equalization (BPDHE) method is actually an extension to both MPHEBP and DHE. Weighting mean-separated sub-histogram equalization (WMSHE) method is to perform the effective contrast enhancement of the digital image.

Reducing scanning electron microscope charging by using exponential contrast stretching technique on post‐processing images

An exponential contrast stretching (ECS) technique is developed to reduce the charging effects on scanning electron microscope images. Compared to some of the conventional histogram equalization methods, such as bi-histogram equalization and recursive mean-separate histogram equalization, the proposed ECS method yields better image compensation. Diode sample chips with insulating and conductive surfaces are used as test samples to evaluate the efficiency of the developed algorithm. The algorithm is implemented in software with a frame grabber card, forming the front-end video capture element.

Adaptive contrast enhancement using gain-controllable clipped histogram equalization

Histogram equalization is a simple and effective method for contrast enhancement as it can automatically define the intensity transformation function based on statistical characteristics of the image. However, it tends to alter the brightness of the entire image, which it is not suitable for consumer electronic products, where preservation of the original brightness is essential to avoid annoying artifacts. This paper presents a new contrast enhancement method for generalization of the existing bihistogram equalization (BHE) and recursive mean-separate histogram equalization (RMSHE) methods. The proposed method is referred to gain-controllable clipped histogram equalization (GC-CHE) to provide both histogram equalization and brightness preservation. More specifically adaptive contrast enhancement is realized by using clipped histogram equalization with controllable gain. The clipping rate is determined based on the mean brightness, and the clipping threshold is determined based on the clipping rate. The clipping rate is adaptively controlled to enhance the contrast with preserving the mean brightness. It is mathematically proven that the mean brightness of the output image converges to that of the input image with adaptive controlled. Simulation results show that the proposed GC-CHE method outperforms existing histogram-based methods, such as HE, BHE, and RMSHE, in various situations.

Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement

This paper proposes a new histogram equalization method, called RSWHE (recursively separated and weighted histogram equalization), for brightness preservation and image contrast enhancement. The essential idea of RSWHE is to segment an input histogram into two or more sub-histograms recursively, to modify the sub-histograms by means of a weighting process based on a normalized power law function, and to perform histogram equalization on the weighted sub-histograms independently. RSIHE (recursive sub-image histogram equalization) and RMSHE (recursive mean separate histogram equalization) are some methods similar to RSWHE, but they do not carry out the above weighting process. We show that compared to other existent methods, RSWHE preserves the image brightness more accurately and produces images with better contrast enhancement.

Recursive sub-image histogram equalization applied to gray scale images

A novel recursive sub-image histogram equalization (RSIHE) is developed to overcome the drawbacks of generic histogram equalization (HE) for gray scale images. Compared to some of the conventional HE methods, such as bi-histogram equalization and recursive mean-separate histogram equalization, the proposed RSIHE method yields better image compensation. The scanning electron microscope images are used as test images to evaluate the efficiency of the developed algorithm. The algorithm is implemented in software with a frame grabber card, forming the front-end video capture element.

Preserving brightness in histogram equalization based contrast enhancement techniques

Histogram equalization (HE) has been a simple yet effective image enhancement technique. However, it tends to change the brightness of an image significantly, causing annoying artifacts and unnatural contrast enhancement. Brightness preserving bi-histogram equalization (BBHE) and dualistic sub-image histogram equalization (DSIHE) have been proposed to overcome these problems but they may still fail under certain conditions. This paper proposes a novel extension of BBHE referred to as minimum mean brightness error bi-histogram equalization (MMBEBHE). MMBEBHE has the feature of minimizing the difference between input and output image's mean. Simulation results showed that MMBEBHE can preserve brightness better than BBHE and DSIHE. Furthermore, this paper also formulated an efficient, integer-based implementation of MMBEBHE. Nevertheless, MMBEBHE also has its limitation. Hence, this paper further proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE). RMSHE is featured with scalable brightness preservation. Simulation results showed that RMSHE is the best compared to HE, BBHE, DSIHE, and MMBEBHE.

Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation

Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extend. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE) to provide not only better but also scalable brightness preservation. BBHE separates the input image's histogram into two based on its mean before equalizing them independently. While the separation is done only once in BBHE, this paper proposes to perform the separation recursively; separate each new histogram further based on their respective mean. It is analyzed mathematically that the output image's mean brightness will converge to the input image's mean brightness as the number of recursive mean separation increases. Besides, the recursive nature of RMSHE also allows scalable brightness preservation, which is very useful in consumer electronics. Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), have been properly enhanced by RMSHE.