東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Computer-aided detection of breast c...

Guo, Yanhui.

FindBook

Google Book

Amazon

博客來

Computer-aided detection of breast cancer using ultrasound images.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computer-aided detection of breast cancer using ultrasound images./
作者:	Guo, Yanhui.
面頁冊數:	130 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3759.
Contained By:	Dissertation Abstracts International71-06B.
標題:	Health Sciences, Radiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3404289
ISBN:	9781124010755

Computer-aided detection of breast cancer using ultrasound images.
Guo, Yanhui.

Computer-aided detection of breast cancer using ultrasound images. - 130 p.

Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3759.

Thesis (Ph.D.)--Utah State University, 2010.

Ultrasound imaging suffers from severe speckle noise. We propose a novel approach for speckle reduction using 2D homogeneity and directional average filters to remove speckle noise. We transform speckle noise into additive noise using a logarithm transformation. Texture information is employed to describe the speckle characteristics of the image. The homogeneity value is defined using texture information value, and the ultrasound image is transformed into a homogeneity domain from the gray domain. If the homogeneity value is high, the region is homogenous and has less speckle noise. Otherwise, the region is nonhomogenous, and speckle noise occurs. The threshold value is employed to distinguish homogenous regions from regions with speckle noise obtained from a 2D homogeneity histogram according to the maximal entropy principle. A new directional filtering is convoluted to remove noise from pixels in a nonhomogenous region. The filtering processing iterates until the breast ultrasound image is homogenous enough. Experiments show the proposed method improves denoising and edge-preserving capability.

ISBN: 9781124010755Subjects--Topical Terms:

1019076
Health Sciences, Radiology.

Computer-aided detection of breast cancer using ultrasound images.
LDR:03685nam 2200361 4500 001 1399833
005 20110930095843.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124010755
035 $a (UMI)AAI3404289
035 $a AAI3404289
040 $a UMI $c UMI
100 1 $a Guo, Yanhui. $3 1678846
245 1 0 $a Computer-aided detection of breast cancer using ultrasound images.
300 $a 130 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3759.
500 $a Adviser: Heng-Da Cheng.
502 $a Thesis (Ph.D.)--Utah State University, 2010.
520 $a Ultrasound imaging suffers from severe speckle noise. We propose a novel approach for speckle reduction using 2D homogeneity and directional average filters to remove speckle noise. We transform speckle noise into additive noise using a logarithm transformation. Texture information is employed to describe the speckle characteristics of the image. The homogeneity value is defined using texture information value, and the ultrasound image is transformed into a homogeneity domain from the gray domain. If the homogeneity value is high, the region is homogenous and has less speckle noise. Otherwise, the region is nonhomogenous, and speckle noise occurs. The threshold value is employed to distinguish homogenous regions from regions with speckle noise obtained from a 2D homogeneity histogram according to the maximal entropy principle. A new directional filtering is convoluted to remove noise from pixels in a nonhomogenous region. The filtering processing iterates until the breast ultrasound image is homogenous enough. Experiments show the proposed method improves denoising and edge-preserving capability.
520 $a We present a novel enhancement algorithm based on fuzzy logic to enhance the fine details of ultrasound image features, while avoiding noise amplification and over-enhancement. We take into account both the fuzzy nature of an ultrasound and feature regions on images, which are significant in diagnosis. The maximal entropy principle utilizes the gray-level information to map the image into fuzzy domain. Edge and textural information is extracted in fuzzy domain to describe the features of lesions. The contrast ratio is computed and modified by the local information. Finally, the defuzzification operation transforms the enhanced ultrasound images back to the spatial domain. Experimental results confirm a high enhancement performance including fine details of lesions, without over- or under-enhancement.
520 $a Identifying object boundaries in ultrasound images is a difficult task. We present a novel automatic segmentation algorithm based on characteristics of breast tissue and eliminating particle swarm optimization (EPSO) clustering analysis, thus transforming the segmentation problem into clustering analysis. Mammary gland characteristics in ultrasound images are utilized, and a step-down threshold technique is employed to locate the mammary gland area. Experimental results demonstrate that the proposed approach increases clustering speed and segments the mass from tissue background with high accuracy.
590 $a School code: 0241.
650 4 $a Health Sciences, Radiology. $3 1019076
650 4 $a Computer Science. $3 626642
690 $a 0574
690 $a 0984
710 2 $a Utah State University. $b Computer Science. $3 1032121
773 0 $t Dissertation Abstracts International $g 71-06B.
790 1 0 $a Cheng, Heng-Da, $e advisor
790 1 0 $a Qi, Xiaojun $e committee member
790 1 0 $a Dyreson, Curtis $e committee member
790 1 0 $a Allan, Stephen J. $e committee member
790 1 0 $a Chen, Yangquan $e committee member
790 1 0 $a Burnham, Byron R. $e committee member
790 $a 0241
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3404289