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Content Based Mammogram Image Retrieval Based on the Multiclass Visual Problem

Siyahjani, Farzad | 2011

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 41461 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Fatemizadeh, Emad
  7. Abstract:
  8. In recent years there has been a great effort to enhance the computer-aided diagnosis systems, Since expertise elicited from past resolved cases plays an important role in medical applications, and images acquired from various cases have a great contribution to diagnosis of the abnormalities, Content based medical image retrieval has become an active research area for many scientists. In this project we proposed a new framework to retrieve visually similar images from a large database, in which visual similarity is regarded as much as the semantic category relevance, we used optimized wavelet transform as the multi-resolution analysis of the images and extracted various statistical SGLDM features, then by reducing feature space, we used error correcting codes in order to untwist the existing multiclass visual problem introduced in preceding parts of the report. Also In this project we used textures features extracted from Images described above in a new framework to compare similarity of various images, we designed a decision making machine in which utilizes sparse representation technique to preserve semantic category relevance and visual similarity among the retrieved images and the query image, we used sparse representation to find the most linearly dependent images from database to retrieve, this machine also consists of an optimization framework to optimize wavelets bases (using lifting scheme) to extract appropriate visual features in order to grasp visual content of the images. We implemented our algorithm on the DDSM database which consists of 2500 studies and their annotations provided by specialists.
  9. Keywords:
  10. Image Retrieval ; Sparse Decomposition ; Image Retrieval ; Content Based Retrieval ; Computer Aided Design (CAD) ; Lifting Scheme ; Error Correction Codes ; Mammography

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