Dr. James P. Monaco Profile

Dr. James P. Monaco

SPIE Involvement:

Conference Program Committee | Author

SPIE Journal Paper | 28 March 2017

Connecting Markov random fields and active contour models: application to gland segmentation and classification

Jun Xu, James Monaco, Rachel Sparks, Anant Madabhushi

JMI, Vol. 4, Issue 02, 021107, (March 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.2.021107

KEYWORDS: Tissues, Image segmentation, Performance modeling, Magnetorheological finishing, Prostate, Data modeling, Actinium, Tumor growth modeling, Visual process modeling, Statistical modeling

Read Abstract +

Proceedings Article | 27 March 2009 Paper

Probabilistic pairwise Markov models: application to prostate cancer detection

James Monaco, John Tomaszewski, Michael Feldman, Mehdi Moradi, Parvin Mousavi, Alexander Boag, Chris Davidson, Purang Abolmaesumi, Anant Madabhushi

Proceedings Volume 7259, 725903 (2009) https://doi.org/10.1117/12.812462

KEYWORDS: Image segmentation, Solid modeling, Tumor growth modeling, CAD systems, Computer aided design, Magnetorheological finishing, Prostate, Prostate cancer, Computer aided diagnosis and therapy, Chromium

Read Abstract +

Proceedings Article | 17 March 2008 Paper

Improving supervised classification accuracy using non-rigid multimodal image registration: detecting prostate cancer

Jonathan Chappelow, Satish Viswanath, James Monaco, Mark Rosen, John Tomaszewski, Michael Feldman, Anant Madabhushi

Proceedings Volume 6915, 69150V (2008) https://doi.org/10.1117/12.770703

KEYWORDS: Magnetic resonance imaging, Image registration, Curium, Cancer, Prostate, Computer aided diagnosis and therapy, Prostate cancer, Image segmentation, Independent component analysis, Image classification

Read Abstract +

Computer-aided diagnosis (CAD) systems for the detection of cancer in medical images require precise labeling of training data. For magnetic resonance (MR) imaging (MRI) of the prostate, training labels define the spatial extent of prostate cancer (CaP); the most common source for these labels is expert segmentations. When ancillary data such as whole mount histology (WMH) sections, which provide the gold standard for cancer ground truth, are available, the manual labeling of CaP can be improved by referencing WMH. However, manual segmentation is error prone, time consuming and not reproducible. Therefore, we present the use of multimodal image registration to automatically and accurately transcribe CaP from histology onto MRI following alignment of the two modalities, in order to improve the quality of training data and hence classifier performance. We quantitatively demonstrate the superiority of this registration-based methodology by comparing its results to the manual CaP annotation of expert radiologists. Five supervised CAD classifiers were trained using the labels for CaP extent on MRI obtained by the expert and 4 different registration techniques. Two of the registration methods were affi;ne schemes; one based on maximization of mutual information (MI) and the other method that we previously developed, Combined Feature Ensemble Mutual Information (COFEMI), which incorporates high-order statistical features for robust multimodal registration. Two non-rigid schemes were obtained by succeeding the two affine registration methods with an elastic deformation step using thin-plate splines (TPS). In the absence of definitive ground truth for CaP extent on MRI, classifier accuracy was evaluated against 7 ground truth surrogates obtained by different combinations of the expert and registration segmentations. For 26 multimodal MRI-WMH image pairs, all four registration methods produced a higher area under the receiver operating characteristic curve compared to that obtained from expert annotation. These results suggest that in the presence of additional multimodal image information one can obtain more accurate object annotations than achievable via expert delineation despite vast differences between modalities that hinder image registration.

Conference Committee Involvement (14)