Methodology for evaluation of image segmentation algorithms on medical images

1996 ◽  
Author(s):  
Vikram Chalana ◽  
Yongmin Kim
Keyword(s):  
Image Segmentation ◽  
Medical Images ◽  
Segmentation Algorithms

PERFORMANCE EVALUATION OF REGION-GROWING BASED SEGMENTATION ALGORITHMS FOR SEGMENTING THE AORTA

2016 ◽  
Vol 78 (4-3) ◽  
Author(s):  
Hussain Rahman ◽  
Fakhrud Din ◽  
Sami ur Rahmana ◽  
Sehatullah Sehatullah
Keyword(s):  
Image Segmentation ◽  
Medical Images ◽  
Region Growing ◽  
Medical Image Segmentation ◽  
Ease Of Use ◽  
Daily Routine ◽  
Segmentation Algorithms ◽  
Abdominal Mri ◽  
Mri Scans ◽  
The Common

Region-growing based image segmentation techniques, available for medical images, are reviewed in this paper. In digital image processing, segmentation of humans' organs from medical images is a very challenging task. A number of medical image segmentation techniques have been proposed, but there is no standard automatic algorithm that can generally be used to segment a real 3D image obtained in daily routine by the clinicians. Our criteria for the evaluation of different region-growing based segmentation algorithms are: ease of use, noise vulnerability, effectiveness, need of manual initialization, efficiency, computational complexity, need of training, information used, and noise vulnerability. We test the common region-growing algorithms on a set of abdominal MRI scans for the aorta segmentation. The evaluation results of the segmentation algorithms show that region-growing techniques can be a better choice for segmenting an object of interest from medical images.

A Study on Various Image Segmentation Algorithms

2018 ◽  
pp. 272-276
Author(s):  
S. DivyaMeena ◽  
M. Mangaleswaran
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Images ◽  
Large Data ◽  
Performance Measure ◽  
Medical Image Segmentation ◽  
3D Image ◽  
Medical Field ◽  
Segmentation Algorithms ◽  
Mri Image

Medical images have made a great effect on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Medical Image Segmentation is the development of programmed or semi-automatic detection of limitations within a 2D or 3D image. In medical field, image segmentation is one of the vital steps in Image identification and Object recognition. Image segmentation is a method in which large data is partitioned into small amount of data. If the input MRI image is segmented then identifying the lump attacked region will be easier for physicians. In recent days, many algorithms are proposed for the image segmentation. In this paper, an analysis is made on various segmentation algorithms for medical images. Furthermore, a comparison of existing segmentation algorithms is also discussed along with the performance measure of each.

scholarly journals An Overview of Segmentation Algorithms for the Analysis of Anomalies on Medical Images

2018 ◽  
Vol 29 (1) ◽  
pp. 612-625 ◽  
Author(s):  
Subbiahpillai Neelakantapillai Kumar ◽  
Alfred Lenin Fred ◽  
Paul Sebastin Varghese
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Images ◽  
Computation Time ◽  
Vital Role ◽  
Medical Image Segmentation ◽  
Body Parts ◽  
Medical Practitioners ◽  
Segmentation Algorithms ◽  
The Right

Abstract Human disease identification from the scanned body parts helps medical practitioners make the right decision in lesser time. Image segmentation plays a vital role in automated diagnosis for the delineation of anatomical organs and anomalies. There are many variants of segmentation algorithms used by current researchers, whereas there is no universal algorithm for all medical images. This paper classifies some of the widely used medical image segmentation algorithms based on their evolution, and the features of each generation are also discussed. The comparative analysis of segmentation algorithms is done based on characteristics like spatial consideration, region continuity, computation complexity, selection of parameters, noise immunity, accuracy, and computation time. Finally, in this work, some of the typical segmentation algorithms are implemented on real-time datasets using Matlab 2010 software, and the outcome of this work will be an aid for the researchers in medical image processing.

scholarly journals Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 144
Author(s):  
Yuexing Han ◽  
Xiaolong Li ◽  
Bing Wang ◽  
Lu Wang
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Medical Images ◽  
Tumor Segmentation ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Segmentation Accuracy ◽  
Boundary Information

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.

scholarly journals A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM

2011 ◽  
Vol 07 (01) ◽  
pp. 155-171 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Segmentation ◽  
Point Of View ◽  
Neutrosophic Set ◽  
Convergence Criterion ◽  
Complex Objects ◽  
Fuzzy C Means ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Fuzzy C Means Algorithm

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

Comparison of Thresholding and Edge Detection Segmentation Techniques

2013 ◽  
Vol 860-863 ◽  
pp. 2783-2786
Author(s):  
Yu Bing Dong ◽  
Hai Yan Wang ◽  
Ming Jing Li
Keyword(s):  
Image Segmentation ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Experimental Results ◽  
Mean Square ◽  
Segmentation Method ◽  
Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

scholarly journals Image Segmentation Techniques in Bone Structure Psychiatry

2020 ◽  
Vol 9 (07) ◽  
pp. 25102-25112
Author(s):  
Ajayi Olayinka Adedoyin ◽  
Olamide Timothy Tawose ◽  
Olu Sunday Adetolaju
Keyword(s):  
Image Segmentation ◽  
Bone Structure ◽  
Performance Comparison ◽  
Imaging Systems ◽  
Intrinsic Properties ◽  
X Ray ◽  
Practical Applications ◽  
X Ray Imaging ◽  
Segmentation Algorithms ◽  
Bone Structures

Today, a large number of x-ray images are interpreted in hospitals and computer-aided system that can perform some intelligent task and analysis is needed in order to raise the accuracy and bring down the miss rate in hospitals, particularly when it comes to diagnosis of hairline fractures and fissures in bone joints. This research considered some segmentation techniques that have been used in the processing and analysis of medical images and a system design was proposed to efficiently compare these techniques. The designed system was tested successfully on a hand X-ray image which led to the proposal of simple techniques to eliminate intrinsic properties of x-ray imaging systems such as noise. The performance and accuracy of image segmentation techniques in bone structures were compared and these eliminated time wasting on the choice of image segmentation algorithms. Although there are several practical applications of image segmentation such as content-based image retrieval, machine vision, medical imaging, object detection, recognition tasks, etc., this study focuses on the performance comparison of several image segmentation techniques for medical X-ray images.

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