Research and Application on Chromosome Band Extraction
0
Citation
0
Reference
20
Related Paper
Abstract:
A threshold based approach for chromosome band extraction is introduced based on the grey scale variance within chromosome.Regional threshold is calculated with modified Bernsen algorithm firstly.Then grey-level classification is implemented and chromosome region is sharpened after iterative dichotomous processing.All separated regions are labeled for every chromosome.Finally,chromosome band is obtained by density latitude selection in the histogram of sharpened result.Experimental results from 200 chromosome samples with the proposed method demonstrate that the proposed method extracts chromosome band precisely and noise-freely,and is helpful for the research of automatic chromosome segmentation.Keywords:
Chromosome Band
Cite
Aiming at the problem of wrong segmentation in common 2-D histogram region division,in order to meet the requirement of different images and segmentation objectives,the 2-D linear-type minimum error threshold segmentation method was generalized,and a much more widely suitable thresholding method was proposed based on 2-D histogram θ-division and minimum error.The threshold selection formulae and its fast recursive algorithm were deduced.The influence of different θ values on segmented results and running time was analyzed according to the experimental results.Compared with the conventional 2-D minimum error method,the proposed method not only achieves more accurate segmented result and more robust anti-noise,but also significantly reduces the running time.The linear-type minimum error threshold segmentation method is only a special case with θ=45° of the proposed method.
Balanced histogram thresholding
Image histogram
Cite
Citations (1)
In this paper, we proposed a new histogram-based threshold point automatic extraction method. In accordance with the intuitionistic features of the histogram, we calculated statistical histogram peaks and valleys, and selected the larger peak as a reference. And then, we selected the largest gap in the area which was composed of a selected larger value point and one of the peak points which had not been selected. Finally, we selected the largest collection of the maximum gap. This point was the threshold point.
Threshold limit value
Value (mathematics)
Cite
Citations (0)
Iterative histogram matching algorithm for chromosome image enhancement based on statistical moments
Vivid banding pattern of the chromosome image is a crucial part for diagnosis in karyotype medical test. Furthermore, thriving computer aided segmentation and classification depend on the initial image quality. In this paper, we propose an adaptive and iterative histogram matching algorithm for chromosome contrast enhancement especially in banding patterns which is one of the most important information laid in chromosome image. Objective histogram, with which the initial image needs to be matched, is created based on processes on the initial image histogram. Calculation of statistical moments of image histogram and determination of parameters in each step of iteration based on these moments form the algorithm structure. In order to assess the performance of the proposed algorithm, Density Profile (DP) and the contrast improvement ratio (CIR), are utilized. Experimental results indicate that the proposed method shows better results in both quantitative gauges of CIR and DP, and visual perception.
Image histogram
Adaptive histogram equalization
Cite
Citations (7)
The concept of the two-dimensional bound histogram is proposed,and an automatic threshold selection method based on that histogram is presented.In the method,the bound set of an image and its corresponding two-dimensional bound histogram are constructed and the gray-level threshold for segmentation is determined by the Otsu algorithm based on that histogram.In order to prove the validity of the method,it was applied to thresholding of the image of a small underwater target.The Otsu algorithm based on the one-dimensional bound histogram was also used.The results show that the former,the proposed method,is well applicable to the image with a non-ideal bimodal histogram,and has better antinoise performance nad segmental effect than the latter.
Balanced histogram thresholding
Otsu's method
Adaptive histogram equalization
Image histogram
Region growing
Cite
Citations (2)
This paper presents a simple and effective segmentation method for color image. All possible uniform regions in the color image are obtained by using the histogram threshold technique. That is, the main peak values is found in order to initialize and merge the regions and obtain the uniform regions marked by the corresponding clustering centers. The adaptive K-means clustering algorithm is given to improve compactness of the formed uniform regions. The experimental results show that, compared with IAFHA(improved ant colony-Fuzzy C-means hybrid algorithm)method, the proposed method can receive the less segmentation regions, fast segmentation speed and have certain robustness.
Region growing
Segmentation-based object categorization
Range segmentation
Robustness
K-Means Clustering
Cite
Citations (1)
Threshold based segmentation methods can effectively separate objects from the background. The conventional Otsu's method can achieve good result only when the histogram of an image has two distinct peaks. In this paper, an iterative thresholding based on 2D improved Otsu method using a novel threshold value recognition function is proposed which is used to find the optimum threshold value in different types of histograms and separate into two classes. For the two classes separated by the above threshold value, mean values are computed. Based on the threshold value and the two mean values, the histogram of an image is divided iteratively into three regions, namely, the Foreground (FG) region, Background (BG) region and To-Be-Processed (TBP) region. The pixels greater than the larger mean belongs to FG region, the pixel which are lesser than the smaller mean belongs to BG region. The pixel that lies between the two mean values belongs to the TBP region and only this region is processed at next iteration, the FG and BG region are not processed further. Again for that TBP region, the novel threshold value recognition function is used to find a new threshold value and then the two mean values are computed to separate into three classes, namely, the FG, BG and TBP region. Then the new TBP region is processed in a similar manner iteratively until the two mean values computed at successive iterations are not changed. Finally all the previously determined foreground regions are combined separately and the background regions are combined separately to give the segmented image. The experimental results show that the proposed method performs well in detecting the objects and has better anti-noise performance compared with the existing methods.
Otsu's method
Balanced histogram thresholding
Region growing
Threshold limit value
Cite
Citations (14)
This paper proposes a new text candidate region detection method that uses genetic algorithm based on information of the segmented regions. In image segmentation, a classification of the pixels at each color channel and a reclassification of the region-unit for reducing inhomogeneous clusters are performed. EWFCM(Entropy-based Weighted C-Means) algorithm to classify the pixels at each color channel is an improved FCM algorithm added with spatial information, and therefore it removes the meaningless regions like noise. A region-based reclassification based on a similarity between each segmented region of the most inhomogeneous cluster and the other clusters reduces the inhomogeneous clusters more efficiently than pixel- and cluster-based reclassifications. And detecting text candidate regions is performed by genetic algorithm based on energy and variance of the directional edge components, the number, and a size of the segmented regions. The region information-based detection method can singles out semantic text candidate regions more accurately than pixel-based detection method and the detection results will be more useful in recognizing the text regions hereafter. Experiments showed the results of the segmentation and the detection. And it confirmed that the proposed method was superior to the existing methods.
Region growing
Cite
Citations (0)
For color image, an adaptive vector threshold segmentation algorithm in which the threshold is a vector is presented in this paper. Image segmentation is divided into two steps; difference vector of pixel vector of image and threshold vector is computed at first and a pixel is confirmed which region ( object region or background region) it belongs to finally. At the same time, the method for selecting optimal threshold vector called extended class vector square error is given based on class square error (Ostu method). Therefore, adaptive vector threshold segmentation algorithm for color image is realized. Experiments results show that the image segmentation algorithm presented in this paper is better than the threshold segmentation for gray image.
Segmentation-based object categorization
Region growing
Cite
Citations (0)
This paper proposes Multidirectional Block Ranking based segmentation and removal of Interphase cells from chromosome images. The efficiency of automatic karyotyping decreases with the presence of undivided, condensed mass of chromosomes called Interphase cells, stain, debris and other unwanted interferences in the chromosome image. The algorithm segments and removes these interferences and enhances the accuracy of automatic karyotyping. The method is tested and excellent segmentation accuracy is accomplished. The chromosome image is preprocessed and a boundary-mapping algorithm is applied to identify the Region of Interest (RoI). The image is divided into blocks and ranks are assigned to all the blocks using Gaussian Ranking Functions (GRF) based on their contribution to the RoI. A higher rank is assigned to the block contributing more while a lesser rank is assigned to other blocks that contribute less to the RoI. The Interphase cells that constitute the RoI are removed from the chromosome images based on the cumulative rank obtained by the blocks in the chromosome image. The proposed algorithm is applied to segment and remove Interphase cells, stains, dirt and other particles that exhibit structural homogeneity. The algorithm gives accurate results in applications where the RoI to be segmented share the grey level with the background wherein the traditional image segmentation methods fall short of accomplishing precise segmentation results.
Interphase
Region of interest
Cite
Citations (8)
In order to analyze gene chip image better,along with extract the data information as accurately as possible,to describe the gene sample,this research paper proposes to implement a minimum error threshold segmentation method.Based on the assumption that the distributions of object and background are governed by a mixture normal distribution,this method sets an objective function of minimum error classification.This method also allows for the implementation of the segmentation between gene sample and background image through calculating the optimal segmentation threshold by minimizing the objective function.Next,the feature data from the segment of gene sample image was extracted and a clustering analysis with the data was done to realize the successful classification of the experimental samples.The study examined two groups of gene chip images and analyzed them by using this method in the experiment.The results show that the classification result was better and the feasibility of the analysis method was verified.
Sample (material)
Feature (linguistics)
Segmentation-based object categorization
Cite
Citations (0)