Comput. Biol. Med. - Tumor segmentation from computed tomography image data using a probabilistic pixel selection approach.

Tópicos

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Resumo

Automatic segmentation of tumors is a complicated and difficult process as most tumors are rarely clearly delineated from healthy tissues. A new method for probabilistic segmentation to efficiently segment tumors within CT data and to improve the use of digital medical data in diagnosis has been developed. Image data are first enhanced by manually setting the appropriate window center and width, and if needed a sharpening or noise removal filter is applied. To initialize the segmentation process, a user places a seed point within the object of interest and defines a search region for segmentation. Based on the pixels' spatial and intensity properties, a probabilistic selection criterion is used to extract pixels with a high probability of belonging to the object. To facilitate the segmentation of multiple slices, an automatic seed selection algorithm was developed to keep the seeds in the object as its shape and/or location changes between consecutive slices. The seed selection algorithm performs a greedy search by searching for pixels with matching intensity close to the location of the original seed point. A total of ten CT datasets were used as test cases, each with varying difficulty in terms of automatic segmentation. Five test cases had mean false positive error rates less than 10%, and four test cases had mean false negative error rates less than 10% when compared to manual segmentation of those tumors by radiologists.

Resumo Limpo

automat segment tumor complic difficult process tumor rare clear delin healthi tissu new method probabilist segment effici segment tumor within ct data improv use digit medic data diagnosi develop imag data first enhanc manual set appropri window center width need sharpen nois remov filter appli initi segment process user place seed point within object interest defin search region segment base pixel spatial intens properti probabilist select criterion use extract pixel high probabl belong object facilit segment multipl slice automat seed select algorithm develop keep seed object shape andor locat chang consecut slice seed select algorithm perform greedi search search pixel match intens close locat origin seed point total ten ct dataset use test case vari difficulti term automat segment five test case mean fals posit error rate less four test case mean fals negat error rate less compar manual segment tumor radiologist

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