TY - GEN
T1 - Multiscale fuzzy C-means image classification for multiple weighted MR images for the assessment of photodynamic therapy in mice
AU - Wang, Hesheng
AU - Feyes, Denise
AU - Mulvihill, John
AU - Oleinick, Nancý
AU - Maclennan, Gregory
AU - Fei, Baowei
PY - 2007
Y1 - 2007
N2 - We are investigating in vivo small animal imaging and analysis methods for the assessment of photodynamic therapy (PDT), an emerging therapeutic modality for cancer treatment. Multiple weighted MR images were acquired from tumor-bearing mice pre- and post-PDT and 24-hour after PDT. We developed an automatic image classification method to differentiate live, necrotic and intermediate tissues within the treated tumor on the MR images. We used a multiscale diffusion filter to process the MR images before classification. A multiscale fuzzy C-means (FCM) classification method was applied along the scales. The object function of the standard FCM was modified to allow multiscale classification processing where the result from a coarse scale is used to supervise the classification in the next scale. The multiscale fuzzy C-means (MFCM) method takes noise levels and partial volume effects into the classification processing. The method was validated by simulated MR images with various noise levels. For simulated data, the classification method achieved 96.0 ±1.1% overlap ratio. For real mouse MR images, the classification results of the treated tumors were validated by histologic images. The overlap ratios were 85.6 ± 5.1%, 82.4 ± 7.8% and 80.5 ± 10.2% for the live, necrotic, and intermediate tissues, respectively. The MR imaging and the MFCM classification methods may provide a useful tool for the assessment of the tumor response to photodynamic therapy in vivo.
AB - We are investigating in vivo small animal imaging and analysis methods for the assessment of photodynamic therapy (PDT), an emerging therapeutic modality for cancer treatment. Multiple weighted MR images were acquired from tumor-bearing mice pre- and post-PDT and 24-hour after PDT. We developed an automatic image classification method to differentiate live, necrotic and intermediate tissues within the treated tumor on the MR images. We used a multiscale diffusion filter to process the MR images before classification. A multiscale fuzzy C-means (FCM) classification method was applied along the scales. The object function of the standard FCM was modified to allow multiscale classification processing where the result from a coarse scale is used to supervise the classification in the next scale. The multiscale fuzzy C-means (MFCM) method takes noise levels and partial volume effects into the classification processing. The method was validated by simulated MR images with various noise levels. For simulated data, the classification method achieved 96.0 ±1.1% overlap ratio. For real mouse MR images, the classification results of the treated tumors were validated by histologic images. The overlap ratios were 85.6 ± 5.1%, 82.4 ± 7.8% and 80.5 ± 10.2% for the live, necrotic, and intermediate tissues, respectively. The MR imaging and the MFCM classification methods may provide a useful tool for the assessment of the tumor response to photodynamic therapy in vivo.
KW - Fuzzy C-means
KW - Image classification
KW - Magnetic resonance imaging (MRI)
KW - Multiscale
KW - Photodynamic treatment (PDT)
KW - Small animal imaging
UR - http://www.scopus.com/inward/record.url?scp=36249023689&partnerID=8YFLogxK
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U2 - 10.1117/12.710188
DO - 10.1117/12.710188
M3 - Conference contribution
C2 - 24386526
AN - SCOPUS:36249023689
SN - 0819466301
SN - 9780819466303
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2007
T2 - Medical Imaging 2007: Image Processing
Y2 - 18 February 2007 through 20 February 2007
ER -