A complete breast cancer detection approach via quantitative and qualitative analysis of breast ultrasound images

Kabir, Ertaza Enan; Ashik, Abdullah Salmon; Abid, Rasheed

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dc.contributor.author	Kabir, Ertaza Enan
dc.contributor.author	Ashik, Abdullah Salmon
dc.contributor.author	Abid, Rasheed
dc.date.accessioned	2018-10-01T06:47:20Z
dc.date.available	2018-10-01T06:47:20Z
dc.date.issued	2017-11-15
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dc.identifier.uri	http://hdl.handle.net/123456789/183
dc.description.abstract	Globally, cancer is becoming a major health issue as advances in modern medicine continue to extend the human life span. In the U.S., cancer is the second most-common cause of death, exceeded only by heart disease and accounting for nearly one of every four deaths. Breast cancer ranks second as a cause of cancer death in women (after lung cancer). Thus, early detection and treatment are critical in reducing breast cancer related mortality. Working with Breast ultrasound (BUS) data or image is regarded as a challenging task due to the inherent nature of ultrasound imaging. Ultrasound imaging is characterized by speckle patterns, anisotropy and signal drop-out. Moreover, proper image acquisition techniques by the clinicians and their level of expertise also play a dominant role in determining the image quality. The fuzziness in the shape and boundaries of the breast lesions make it very difficult to automate the segmentation of BUS images. In order to improve the issues prevalent in the existing approaches, a complete qualitative and quantitative analysis of Breast ultrasound (BUS) images is proposed in this thesis. The method involves three steps – (a) Finding out strain image by means of strain estimation, (b) Final Segmentation of the detected lesion and (c) Quantitative analysis of the lesion.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Electronic Engineering, Islamic University of Technology	en_US
dc.title	A complete breast cancer detection approach via quantitative and qualitative analysis of breast ultrasound images	en_US
dc.type	Thesis	en_US