scholarly journals Bone Cancer Detection from X-Ray and MRI Images through Image Segmentation Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 273-278
Keyword(s):  
Image Segmentation ◽  
Cancer Detection ◽  
Image Interpretation ◽  
Bone Cancer ◽  
Region Growing ◽  
Segmentation Method ◽  
X Ray ◽  
Science Paper ◽  
Biomedical Field ◽  
Edge Based

This paper is based on integration of the biomedical field and computer science. Paper contains the study of bone cancer and features to predict the type of the same. Related work to find cancer in human body using computer vision is discussed in this paper. Image segmentation technique like sobel, prewitt, canny, K-means and Region Growing are described in this paper which can be stimulated for X-Ray and MRI image interpretation. Paper also shows the result of edge based and region based image segmentation techniques applied on X-Ray image to detect osteosarcoma cancer present on bone using MATLAB. Finally, paper concluded by finding best suited segmentation method for grey scaled image with future aspects.

Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review

2020 ◽  
Vol 20 (03) ◽  
pp. 2050018
Author(s):  
Neeraj Shrivastava ◽  
Jyoti Bharti
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Magnetic Resonance Images ◽  
Medical Image Segmentation ◽  
Seed Selection ◽  
Seeded Region Growing ◽  
Processing Strategies ◽  
Edge Based ◽  
Seed Points ◽  
Selection Of

In the domain of computer technology, image processing strategies have become a part of various applications. A few broadly used image segmentation methods have been characterized as seeded region growing (SRG), edge-based image segmentation, fuzzy [Formula: see text]-means image segmentation, etc. SRG is a quick, strongly formed and impressive image segmentation algorithm. In this paper, we delve into different applications of SRG and their analysis. SRG delivers better results in analysis of magnetic resonance images, brain image, breast images, etc. On the other hand, it has some limitations as well. For example, the seed points have to be selected manually and this manual selection of seed points at the time of segmentation brings about wrong selection of regions. So, a review of some automatic seed selection methods with their advantages, disadvantages and applications in different fields has been presented.

Enhanced X‐ray image segmentation method using prior shape

2016 ◽  
Vol 11 (2) ◽  
pp. 145-152 ◽  
Author(s):  
Hossein Mahvash Mohammadi ◽  
Jacques A. Guise
Keyword(s):  
Image Segmentation ◽  
Segmentation Method ◽  
X Ray ◽  
Prior Shape

scholarly journals Segmenting radiographs of the hand and wrist using computer vision

1990 ◽  
Author(s):  
Γεώργιος Μάνος
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Bone Age ◽  
Growth Disorders ◽  
Region Merging ◽  
Clinical Tool ◽  
Age Assessment ◽  
Segmentation Methods ◽  
Bone Age Assessment ◽  
Edge Based

"Bone age" age assessment is an important clinical tool in the area of paediatrics. The technique is based upon the appearance and growth of specific bones in a developing child. In particular most methods for "bone age" assessment are based on the examination of the growth of bones of the left hand and wrist on X-ray films. This assessment is useful in the treatment of growth disorders and also is used to predict adult height. One of the most reliable methods for "bone age" assessment is the TW2 method. The drawback of this method is that it is time consuming and therefore its automation is highly desirable. One of the most important aspects of the automation process is image segmentation i.e. the extraction of bones from soft-tissue and background. Over the past 10 years various attempts have been made at the segmentation of handwrist radiographs but with limited success. This can mainly be attributed to the characteristics of the scenes e.g. biological objects, penetrating nature of radiation, faint bone boundaries, uncertainty of scene content, and conjugation of bones. Experience in the field of radiographic image analysis has shown thatsegmentation of radiographic scenes is a difficult task requiring solutions which depend on the nature of the particular problem.There are two main approaches to image segmentation: edge based and region based. Most of the previous attempts at the segmentation of hand-wrist radiographs were edge based. Edge based methods usually require a w-ell defined model of the object boundaries in order to produce successful results. However, for this particular application it is difficult to derive such a model. Region based segmentation methods have produced promising results for scenes which exhibit uncertainty regarding their content and boundaries of objects in the image, as in the case, for example, of natural senes. This thesis presents a segmentation method based on the concept of regions. This method consists of region growing and region merging stages. A technique was developed for region merging which combines edge and region boundai^ information. A bone extraction stage follows which labels regions as either boneor background using heuristic rules based on the grey-level properties of the scene. Finally, a technique is proposed for the segmentation of bone outlines which helps in identifying conjugated bones. Experimental results have demonstrated that this method represents a significant improvement over existing segmentation methods for hand-wrist radiographs, particularly with regard to the segmentation of radiographs with varying degrees of bone maturity.

Fabric Defect Detection Based on SRG-PCNN

2010 ◽  
Vol 148-149 ◽  
pp. 1319-1326 ◽  
Author(s):  
Xiao Shu Si ◽  
Hong Zheng ◽  
Xue Min Hu
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Defect Detection ◽  
Focal Point ◽  
Region Growing ◽  
Segmentation Method ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Defect Image ◽  
Validation Tests

Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors. According to the different features between the normal fabric image and defect image, this paper presents an adaptive image segmentation method based on a simplified region growing pulse coupled neural network (SRG-PCNN) for detecting fabric defects. The validation tests on the developed algorithms were performed with fabric images, and results showed that SRG-PCNN is a feasible and efficient method for defect detection.

Sign in / Sign up

Export Citation Format

Share Document