scholarly journals A Novel Color Reduction Based Image Segmentation Technique For Detection Of Cancerous Region in Breast Thermograms

2015 ◽  
Vol 37 ◽  
pp. 380 ◽  
Author(s):  
Amin Habibi ◽  
Mousa Shamsi
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Image Segmentation ◽  
Color Space ◽  
Important Application ◽  
Experimental Results ◽  
Color Reduction ◽  
Segmentation Technique ◽  
Breast Shape

Segmentation of an image into its components plays an important role in most of the image processing applications. In this article an important application of image processing in determination of Breast Cancer is studied, and A Novel Image Segmentation Technique is proposed in order to determine Cancer in Breast Thermograms. First, this image is converted from RGB to color space HSV. Then Breast shape is extracted by ACM algorithm. Finally, the image has segmented using Color Reduction Based algorithm. Experimental results on the acquired images show Accuracy of the proposed algorithm on the acquired images is over 90% for healthy pixels and defected ones.

FEATURE EXTRACTION BY RANK-ORDER FILTERING FOR IMAGE SEGMENTATION

1988 ◽  
Vol 02 (02) ◽  
pp. 301-319 ◽  
Author(s):  
P. ZAMPERONI
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Density Function ◽  
Rank Order ◽  
Experimental Results ◽  
Textural Features ◽  
Segmentation Method ◽  
Unified Approach

The aim of this paper is to outline a unified approach to feature extraction for segmentation purposes by means of the rank-order filtering of grey values in a neighbourhood of each pixel of a digitized image. In the first section an overview of rank-order filtering for image processing is given, and a fast histogram algorithm is proposed. Section 2 deals with the extraction of a “locally most representative grey value”, defined as the maximum of the local histogram density function. In Section 3 several textural features are described, which can be extracted from the local histogram by means of rank-order filtering, and their properties are discussed. Section 4 formulates some general requirements to be met by the process of image segmentation, and describes a method based upon the features introduced in the former sections. In the last section some experimental results applied to aerial views obtained with the segmentation method of Sect. 4 are reported. These test images have been analyzed within the scope of an investigation centered on terrain recognition for agricultural and ecological purposes.

Research on Low Quality Texture Image Segmentation Background

2014 ◽  
Vol 687-691 ◽  
pp. 3616-3619
Author(s):  
Ning Liu ◽  
Hong Xia Wang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Experimental Results ◽  
Texture Image ◽  
Segmentation Method ◽  
Otsu Thresholding ◽  
Complex Background ◽  
Segmentation Methods ◽  
Texture Image Segmentation

In image processing, the texture image segmentation is one of the most important issues. Considering the problem that the traditional segmentation methods often fail to the low quality texture image segmentation, this paper proposes a modified OTSU thresholding segmentation method. Experimental results show that the proposed method not only is well adapt to the change of brightness and contrast, but also can be applied to much complex background.

scholarly journals Segmentation of TB Bacilli in Ziehl-Neelsen Sputum Slide Images using k-means Clustering Technique

2018 ◽  
Vol 9 (2) ◽  
pp. 63 ◽  
Author(s):  
Rafikha Raof ◽  
M. Y. Mashor ◽  
S. S. M. Noor
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Diagnosis ◽  
Diagnostic System ◽  
Segmentation Method ◽  
Diagnosis System ◽  
Clustering Technique ◽  
Segmentation Technique ◽  
Clustering Approach ◽  
Medical Diagnosis System

Image segmentation is the most crucial steps in determining the accuracy of a medical diagnosis system that is based on image processing procedures. Therefore, it is important to select a suitable image segmentation technique to obtain good results and hence providing optimum accuracy for the developed diagnostic system. In this research, image segmentation procedure using k-means clustering approach has been considered for differentiating between pixels that represent TB bacilli and pixels that represents sputum or background. This paper presents the technique used to separate the TB bacilli and its background from the Ziehl-Neelsen sputum slide images. The k-means clustering has been applied to those images followed by several extra rules. The resulted images show encouraging results, which indicate that the proposed segmentation method is able to filter out the TB bacilli pixels from the background pixels.

Segmentation-Based Automatic White Balance Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 954-957
Author(s):  
Yong Hu
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Color Space ◽  
Color Images ◽  
Experimental Results ◽  
White Balance ◽  
Hsv Color Space ◽  
Low Computational Complexity ◽  
Automatic Adjustment ◽  
Automatic White Balance

Classic automatic white balance algorithm always been invalidity when there are large color-blocks or less of highlights points occurred in cast images. In this literature, an improved automatic adjustment algorithm based on image segmentation is proposed to resolve the problem mentioned above. First, color images were transformed to HSV color space and low saturation area was segmented from S channel. And then, adjustment parameters were calculated by selected points. Experimental results show that the algorithm can effectively correct varied cast image with low computational complexity, and are suitable for various scenarios.

scholarly journals A SURVEY OF ULTRASONOGRAPHY BREAST CANCER IMAGE SEGMENTATION TECHNIQUES

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Jwan N. Saeed
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Image Segmentation ◽  
Active Contour ◽  
Breast Ultrasound ◽  
Common Cause ◽  
Cad Systems ◽  
Detection Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

The most common cause of death among women globally is breast cancer. One of the key strategies to reduce mortality associated with breast cancer is to develop effective early detection techniques. The segmentation of breast ultrasound (BUS) image in Computer-Aided Diagnosis (CAD) systems is critical and challenging. Image segmentation aims to represent the image in a simplified and more meaningful way while retaining crucial features for easier analysis. However, in the field of image processing, image segmentation is a tough task particularly in ultrasound (US) images due to challenges associated with their nature. This paper presents a survey on several techniques of ultrasonography images segmentation including threshold based, region based, watershed, active contour and learning based techniques, their merits, and demerits. This can provide significant insights for CAD developers or researchers to advance this field.

scholarly journals Application of grid-based k-means clustering algorithm for optimal image processing

2012 ◽  
Vol 9 (4) ◽  
pp. 1679-1696 ◽  
Author(s):  
Tingna Shi ◽  
Penglong Wang ◽  
Jeenshing Wang ◽  
Shihong Yue
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Optimal Number ◽  
Number Of Clusters ◽  
General Index ◽  
Benchmark Datasets ◽  
Basic Characteristics ◽  
Grid Based

The effectiveness of K-means clustering algorithm for image segmentation has been proven in many studies, but is limited in the following problems: 1) the determination of a proper number of clusters. If the number of clusters is determined incorrectly, a good-quality segmented image cannot be guaranteed; 2) the poor typicality of clustering prototypes; and 3) the determination of an optimal number of pixels. The number of pixels plays an important role in any image processing, but so far there is no general and efficient method to determine the optimal number of pixels. In this paper, a grid-based K-means algorithm is proposed for image segmentation. The advantages of the proposed algorithm over the existing K-means algorithm have been validated by some benchmark datasets. In addition, we further analyze the basic characteristics of the algorithm and propose a general index based on maximizing grey differences between investigated objective grays and background grays. Without any additional condition, the proposed index is robust in identifying an optimal number of pixels. Our experiments have validated the effectiveness of the proposed index by the image results that are consistent with the visual perception of the datasets.

scholarly journals Seeded Region Growing pada Ruang Warna HSI untuk Segmentasi Citra Ikan Tuna

2017 ◽  
Vol 9 (1) ◽  
pp. 56
Author(s):  
Wanvy Arifha Saputra ◽  
Agus Zainal Arifin
Keyword(s):  
Image Segmentation ◽  
Mathematical Morphology ◽  
Color Space ◽  
Region Growing ◽  
Saturation Intensity ◽  
Seeded Region Growing ◽  
A Value ◽  
Hsi Color Space ◽  
Process Classification

The image of the tuna before entering process classification, it must have a good segmentation results. The result of good segmentation is object and background separate clearly. The image of tuna which has a distribution of light that is uneven and has a complex texture will produce an error segmentation. One method of image segmentation was seeded region growing and parameters that used only two, namely seed and threshold. This research proposed method seeded region growing in the HSI color space for image segmentation of tuna. The Color space of RGB (red green blue) on image of tuna transformed into a color space HSI (hue saturation intensity) then only the hue color space used as segmentation by using seeded region growing. Determination of seed and threshold parameters can do manually and the result of the segmentation do refinement with mathematical morphology. The experiment using 30 image of tuna to segmentation and evaluation methods using RAE (relative foreground area error), MAE (missclassification error) and the MHD (modified Hausdroff distance). The image of the tuna successfully performed segmentation evidenced by a value RAE, ME and MHD respectively are 5,40%, 1,53% dan 0,41%.

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