Dr. Mailan T. Pham Profile

Dr. Mailan T. Pham

at Mount Holyoke College

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Proceedings Article | 21 March 2007 Paper

Content-based image retrieval for pulmonary computed tomography nodule images

Michael Lam, Tim Disney, Mailan Pham, Daniela Raicu, Jacob Furst, Ruchaneewan Susomboon

Proceedings Volume 6516, 65160N (2007) https://doi.org/10.1117/12.710297

KEYWORDS: Computed tomography, Lung, Feature extraction, Image retrieval, Image segmentation, Databases, Image filtering, Content based image retrieval, Matrices, Computing systems

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Research studies have shown that advances in computed tomography (CT) technology allow better detection of pulmonary nodules by generating higher-resolution images. However, the new technology also generates many more individual transversal reconstructions, which as a result may affect the efficiency and accuracy of the radiologists interpreting these images. The goal of our research study is to build a content-based image retrieval (CBIR) system for pulmonary CT nodules. Currently, texture is used to quantify the image content, but any other image feature could be incorporated into the proposed system. Unfortunately, there is no texture model or similarity measure known to work best for encoding nodule texture properties or retrieving most similar nodules. Therefore, we investigated and evaluated several texture models and similarity measures with respect to nodule size, number of retrieved nodules, and radiologist agreement on the nodules' texture characteristic. The results were generated on 90 thoracic CT scans collected by the Lung Image Database Consortium (LIDC). Every case was annotated by up to four radiologists marking the contour of nodules and assigning nine characteristics (including texture) to each identified nodule. We found that Gabor texture descriptors produce the best retrieval results regardless of the nodule size, number of retrieved items or similarity metric. Furthermore, when analyzing the radiologists' agreement on the texture characteristic, we found that when just two radiologists agreed, the average precision increased from 88% to 96% for both Gabor and Markov texture features. Moreover, once three or four radiologists agreed the precision increased to nearly 100%.

Proceedings Article | 3 March 2007 Paper

A comparison of texture models for automatic liver segmentation

Mailan Pham, Ruchaneewan Susomboon, Tim Disney, Daniela Raicu, Jacob Furst

Proceedings Volume 6512, 65124E (2007) https://doi.org/10.1117/12.710422

KEYWORDS: Image segmentation, Liver, Computed tomography, Magnetorheological finishing, Image classification, Tissues, Matrices, Image filtering, Data modeling, Tumor growth modeling

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Automatic liver segmentation from abdominal computed tomography (CT) images based on gray levels or shape alone is difficult because of the overlap in gray-level ranges and the variation in position and shape of the soft tissues. To address these issues, we propose an automatic liver segmentation method that utilizes low-level features based on texture information; this texture information is expected to be homogenous and consistent across multiple slices for the same organ. Our proposed approach consists of the following steps: first, we perform pixel-level texture extraction; second, we generate liver probability images using a binary classification approach; third, we apply a split-and-merge algorithm to detect the seed set with the highest probability area; and fourth, we apply to the seed set a region growing algorithm iteratively to refine the liver's boundary and get the final segmentation results. Furthermore, we compare the segmentation results from three different texture extraction methods (Co-occurrence Matrices, Gabor filters, and Markov Random Fields (MRF)) to find the texture method that generates the best liver segmentation. From our experimental results, we found that the co-occurrence model led to the best segmentation, while the Gabor model led to the worst liver segmentation. Moreover, co-occurrence texture features alone produced approximately the same segmentation results as those produced when all the texture features from the combined co-occurrence, Gabor, and MRF models were used. Therefore, in addition to providing an automatic model for liver segmentation, we also conclude that Haralick cooccurrence texture features are the most significant texture characteristics in distinguishing the liver tissue in CT scans.

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