Image Segmentation Using Hierarchical Cluster Analysis Histogram Thresholding with Local Minima

Nyamlkhagva Sengee,Tak Yoon Oh,Heung Kook Choi 한국멀티미디어학회 2008 한국멀티미디어학회 국제학술대회 Vol.2008 No.-

In this study, we propose a method which is based on "[mage segmentation by histogram thresholding using hierarchical cluster analysis" /HCA/ and "A Nonparametric Approach for Histogram Segmentation" /NHS/. In order to eliminate disadvantages of "HCA" method, we used "NHS" method. The proposed method is not only less computational than "HCA" method because combined method has few clusters but also it uses local minima of histogram which is computed by "NHS".

AN AUTOMATIC METHOD FOR IMAGE CONTRAST ENHANCEMENT BY HISTOGRAM WEIGHT CLUSTERING

Nyamlkhagva Sengee,Byambaragchaa Bazarragchaa,Choi Heung Kook 한국멀티미디어학회 2008 한국멀티미디어학회 학술발표논문집 Vol.2008 No.2

Histogram equalization (GHE) is a simple and well known method in contrast enhancement techniques. There are extensions of GHE to preserve image brightness. Although they can preserve a brightness of original image more than GHE, they couldn't enhance a visualization of original image on some images. Therefore, we propose a new method which not only can preserve the brightness of original image but also can enhance the visualization of original image. The proposed method is called 'Brightness Preserving Weight Clustering Histogram Equalization' (BPWCHE). BPWCHE assigns each non zero bins of original image's histogram to one cluster, and computes every cluster's weight. Then, in order to reduce cluster number, we use three criterions (cluster weight, weight ratio and width of two neighbor clusters) to merge two neighbor clusters. The clusters acquire same partitions of result image histogram. Finally, transformation functions of each cluster's sub-histogram are calculated based on traditional GHE method in their new acquired partitions of result image histogram, and the sub-histogram's gray levels are mapped to the result image by the transformation functions of sub-histograms, correspondingly. As experimental results, BPWCHE can preserve image brightness and enhance visualization of image more effective than GHE and other brightness preserving method.

Contrast Enhancement using Histogram Equalization with a New Neighborhood Metrics

Sengee, Nyamlkhagva,Choi, Heung-Kook Korea Multimedia Society 2008 멀티미디어학회논문지 Vol.11 No.6

In this paper, a novel neighborhood metric of histogram equalization (HE) algorithm for contrast enhancement is presented. We present a refinement of HE using neighborhood metrics with a general framework which orders pixels based on a sequence of sorting functions which uses both global and local information to remap the image greylevels. We tested a novel sorting key with the suggestion of using the original image greylevel as the primary key and a novel neighborhood distinction metric as the secondary key, and compared HE using proposed distinction metric and other HE methods such as global histogram equalization (GHE), HE using voting metric and HE using contrast difference metric. We found that our method can preserve advantages of other metrics, while reducing drawbacks of them and avoiding undesirable over-enhancement that can occur with local histogram equalization (LHE) and other methods.

Contrast Enhancement for Segmentation of Hippocampus on Brain MR Images

Sengee, Nyamlkhagva,Sengee, Altansukh,Adiya, Enkhbolor,Choi, Heung-Kook Korea Multimedia Society 2012 멀티미디어학회논문지 Vol.15 No.12

An image segmentation result depends on pre-processing steps such as contrast enhancement, edge detection, and smooth filtering etc. Especially medical images are low contrast and contain some noises. Therefore, the contrast enhancement and noise removal techniques are required in the pre-processing. In this study, we present an extension by a novel histogram equalization in which both local and global contrast is enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Most important is that original image information can be used for both global brightness preserving and local contrast enhancement, and image quality improvement filtering. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.

An Experimental Study of Image Thresholding Based on Refined Histogram using Distinction Neighborhood Metrics

Nyamlkhagva Sengee,Dalaijargal Purevsuren,Tserennadmid Tumurbaatar 한국멀티미디어학회 2022 The journal of multimedia information system Vol.9 No.2

In this study, we aimed to illustrate that the thresholding method gives different results when tested on the original and the refined histograms. We use the global thresholding method, the well-known image segmentation method for separating objects and background from the image, and the refined histogram is created by the neighborhood distinction metric. If the original histogram of an image has some large bins which occupy the most density of whole intensity distribution, it is a problem for global methods such as segmentation and contrast enhancement. We refined the histogram to overcome the big bin problem in which sub-bins are created from big bins based on distinction metric. We suggest the refined histogram for preprocessing of thresholding in order to reduce the big bin problem. In the test, we use Otsu and medianbased thresholding techniques and experimental results prove that their results on the refined histograms are more effective compared with the original ones.

HISTOGRAM THRESHOLDING USING HIERARCHICAL CLUSTER ANALYSIS WITH LOCAL MINIMA

Nyamlkhagva Sengee,Choi Heung Kook 한국멀티미디어학회 2007 한국멀티미디어학회 학술발표논문집 Vol.2007 No.2

Optimized Brightness Preserving Weight Clustering Histogram Equalization

Nyamlkhagva Sengee,Heung-Kook Choi,Enkhbat Rentsen 한국멀티미디어학회 2009 한국멀티미디어학회 학술발표논문집 Vol.2009 No.1

In this paper, a novel histogram equalization method is presented. Although global histogram equalization (HE) achieves comparatively better performance on almost all types of image, HE sometimes produces excessive visual deterioration. Therefore we propose the new method called "Optimized Brightness Preserving Weight Clustering Histogram Equalization" (OBPWCHE). OBPWCHE divides an input image histogram into clusters using weight criteria and all clusters which are remapped same range on output image histogram are equalized independently by HE. As adjusting the weight criteria, we can adapt the number of clusters. To select optimal number of clusters, we use Pareto optimization method which based on two quality measurements: distortion and brightness. Experimental results show that OBPWCHE can enhance image contrast while preserving image brightness and visualization of images more effectively than HE and other brightness preserving methods without undesirable enhancement.

A Novel Filter ed Bi-Histogram Equalization Method

Nyamlkhagva Sengee,Heung-Kook Choi 한국멀티미디어학회 2015 멀티미디어학회논문지 Vol.18 No.6

A Novel Approach for Brightness Preserving Local Contrast Enhancement based on Distinction Metric

Nyamlkhagva Sengee,Altansukh Sengee,Heung-Kook Choi 한국멀티미디어학회 2011 한국멀티미디어학회 국제학술대회 Vol.2011 No.-

We propose a new extension of bi-histogram equalization called Dualistic Sub-Image Histogram Equalization with Distinction Metric (DSHEDM). DSHEDM consists of two stages. First, large histogram bins that cause washout artifacts are divided into sub-bins using neighborhood metrics; the same intensities of the original image are arranged by neighboring information. In the second stage, the histogram of the original image is separated into two sub-histograms based on the median of the histogram of the original image; the sub-histograms are equalized independently using refined histogram equalization. In an experimental trial, DSHEDM simultaneously preserved the brightness and enhanced the local contrast of the original image.

ENHANCEMENT OF HISTOGRAM EQUALIZATION WITH NEIGHBORHOOD METRICS

Nyamlkhagva Sengee,Tak Yoon O,Kim Tae Yun,Choi Heung Kook 한국멀티미디어학회 2007 한국멀티미디어학회 국제학술대회 Vol.2007 No.-

We present a refinement of histogram equalization using neighborhood metrics with a general framework which orders pixels based on a sequence of sorting functions which uses both global and local information to remap the image greylevels. We designed a novel sorting key with the suggestion of using the original image greylevel as the primary key and a neighborhood voting metric as the secondary key and the novel contrast difference metrics as the third key. We find that our method can provide an improvement in contrast enhancement, can be very flatter histogram. versus global Histogram Equalization (HE) and Histogram equalization with neighborhood metrics (HENM), while avoiding undesirable over-enhancement that can occur with local histogram equalization (LHE) and other methods.