Medical Image Processing for Improved Clinical Diagnosis最新文献

Biomedical Image Processing Software Development for Shoulder Arthroplasty 肩关节置换术生物医学图像处理软件开发

Medical Image Processing for Improved Clinical Diagnosis Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-5876-7.CH001

M. Sadeghi, E. F. Kececi, K. Bilsel, A. Aralaşmak

引用次数: 1

A Survey on Female Breast Cancer 女性乳腺癌调查

Medical Image Processing for Improved Clinical Diagnosis Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-5876-7.CH010

K. Anbarasan, Ramya S.

引用次数: 0

Medical Image Registration in Clinical Diagnosis 临床诊断中的医学图像配准

Medical Image Processing for Improved Clinical Diagnosis Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-5876-7.CH012

A. Swarnambiga, S. Vasuki

引用次数: 0

Non-Subsampled Contourlet Transform-Based Effective Denoising of Medical Images 基于非下采样Contourlet变换的医学图像有效去噪

Medical Image Processing for Improved Clinical Diagnosis Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-5876-7.CH008

P. Karthikeyan, S. Vasuki, K. Karthik

引用次数: 0

Digital Image Analysis in Clinical and Experimental Pathology 临床和实验病理学中的数字图像分析

Medical Image Processing for Improved Clinical Diagnosis Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-5876-7.CH002

D. Meseure, K. D. Alsibai

引用次数: 0

The Fundamentals of Biomedical Image Processing 生物医学图像处理基础

Medical Image Processing for Improved Clinical Diagnosis Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-5876-7.CH009

K. Bhatele, V. Gupta, Kamlesh Gupta, Prashant Shrivastava

引用次数: 0

The Fundamentals of Medical Image Restoration 医学图像恢复的基本原理

Medical Image Processing for Improved Clinical Diagnosis Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-5876-7.CH004

K. Bhatele, D. Tiwari

{"title":"The Fundamentals of Medical Image Restoration","authors":"K. Bhatele, D. Tiwari","doi":"10.4018/978-1-5225-5876-7.CH004","DOIUrl":"https://doi.org/10.4018/978-1-5225-5876-7.CH004","url":null,"abstract":"This chapter simply encapsulates the basics of image restoration, various noise models, and degradation model including some blur and image restoration filters. The mining of high resolution information from the low-resolution images is a very vital task in several applications of digital image processing. In recent times, a lot of research work has been carried out in this field in order to improve the resolution of real medical images especially when the given images are corrupted with some kind of noise. The displayed images are the result of the various stages that might cause imperfections in the digital images, for instance the so-called imaging and capturing process can itself degrade the original scene. The imperfections present in the image need to be studied and analyzed if the noise present in the images is not modelled properly. There are different types of degradations which are considered such as noise, geometrical degradations, imperfections (due to improper illumination and color), and blur. Blurring in the images is generally caused by the relative motion between the camera and the original object being captured or due to poor focusing of an optical system. In the production of aerial photographs for remote sensing purposes, blurs are introduced by the atmospheric turbulence, aberrations in the optical system, and relative motion between the camera and the ground. Apart from the blurring effect, noise also creates imperfections in the images that corrupt the images under analysis. The noise may be introduced by several factors (e.g., medium, recording or capturing system, or by the quantization process). Due to this noise or blur present in the images, resolution needs to be improved and the image is to be restored from the geometrically warped, blurred, and noisy images.","PeriodicalId":358840,"journal":{"name":"Medical Image Processing for Improved Clinical Diagnosis","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124311638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Comparative Study of Medical Image Retrieval Using Distance, Transform, Texture, and Shape 基于距离、变换、纹理和形状的医学图像检索的比较研究

Medical Image Processing for Improved Clinical Diagnosis Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-5876-7.CH011

A. Swarnambiga, S. Vasuki

引用次数: 0

Certain Investigation Titles on the Segmentation of Colon and Removal of Opacified Fluid for Virtual Colonoscopy 虚拟结肠镜下结肠分割及清除混浊液的若干研究题目

Medical Image Processing for Improved Clinical Diagnosis Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-5876-7.CH006

G. Krishnamoorthy, B. Kishore

{"title":"Certain Investigation Titles on the Segmentation of Colon and Removal of Opacified Fluid for Virtual Colonoscopy","authors":"G. Krishnamoorthy, B. Kishore","doi":"10.4018/978-1-5225-5876-7.CH006","DOIUrl":"https://doi.org/10.4018/978-1-5225-5876-7.CH006","url":null,"abstract":"Colorectal cancer (CRC) is a most important type of cancer that can be detected by virtual colonoscopy (VC) in the colon or rectum, and it is the major cause of death prevailing in the world. The CAD technique requires the segmentation of the colon to be accurate and can be implemented by two approaches. The first approach focuses on the segmentation of lungs in the computed tomography (CT) images downloaded from The Cancer Imaging Archive (TCIA) using clustering approach. The second method focused on the automatic segmentation of colon, removal of opacified fluid and bowels for all the slices in a dataset in a sequential order using MATLAB. The second approach requires more computational time, and hence, in order to reduce, the semiautomatic segmentation of colon was implemented in 3D seeded region growing and fuzzy clustering approach in MEVISLAB software. The approaches were implemented in multiple datasets and the accuracy were verified with manual segmentation by radiologist, and the importance of removing opacified fluid were shown for improving the accuracy of colon segments.","PeriodicalId":358840,"journal":{"name":"Medical Image Processing for Improved Clinical Diagnosis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128863462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Feature Selection and Extraction for Eye Disease Diagnosis 眼部疾病诊断的最优特征选择与提取

Medical Image Processing for Improved Clinical Diagnosis Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-5876-7.CH005

P. Alli, S. Somasundaram

引用次数: 0