Full metadata
Title
Noise resilient image segmentation and classification methods with applications in biomedical and semiconductor images
Description
Thousands of high-resolution images are generated each day. Segmenting, classifying, and analyzing the contents of these images are the key steps in image understanding. This thesis focuses on image segmentation and classification and its applications in synthetic, texture, natural, biomedical, and industrial images. A robust level-set-based multi-region and texture image segmentation approach is proposed in this thesis to tackle most of the challenges in the existing multi-region segmentation methods, including computational complexity and sensitivity to initialization. Medical image analysis helps in understanding biological processes and disease pathologies. In this thesis, two cell evolution analysis schemes are proposed for cell cluster extraction in order to analyze cell migration, cell proliferation, and cell dispersion in different cancer cell images. The proposed schemes accurately segment both the cell cluster area and the individual cells inside and outside the cell cluster area. The method is currently used by different cell biology labs to study the behavior of cancer cells, which helps in drug discovery. Defects can cause failure to motherboards, processors, and semiconductor units. An automatic defect detection and classification methodology is very desirable in many industrial applications. This helps in producing consistent results, facilitating the processing, speeding up the processing time, and reducing the cost. In this thesis, three defect detection and classification schemes are proposed to automatically detect and classify different defects related to semiconductor unit images. The first proposed defect detection scheme is used to detect and classify the solder balls in the processor sockets as either defective (Non-Wet) or non-defective. The method produces a 96% classification rate and saves 89% of the time used by the operator. The second proposed defect detection scheme is used for detecting and measuring voids inside solder balls of different boards and products. The third proposed defect detection scheme is used to detect different defects in the die area of semiconductor unit images such as cracks, scratches, foreign materials, fingerprints, and stains. The three proposed defect detection schemes give high accuracy and are inexpensive to implement compared to the existing high cost state-of-the-art machines.
Date Created
2010
Contributors
- Said, Asaad F (Author)
- Karam, Lina (Thesis advisor)
- Chakrabarti, Chaitali (Committee member)
- Tepedelenlioğlu, Cihan (Committee member)
- Patel, Nital (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
xvi, 159 p. : ill. (some col.)
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.8607
Statement of Responsibility
by Asaad F. Said
Description Source
Viewed on Sept. 19, 2012
Level of coding
full
Note
Partial requirement for: Ph.D., Arizona State University, 2010
Note type
thesis
Includes bibliographical references (p. 142-159)
Note type
bibliography
Field of study: Electronic engineering
System Created
- 2011-08-12 12:59:27
System Modified
- 2021-08-30 01:57:21
- 2 years 8 months ago
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