Labeled training data in the medical domain is rare and expensive to obtain. The lack of labeled multimodal medical
image data is a major obstacle for devising learning-based interactive segmentation tools. Transductive learning (TL) or
semi-supervised learning (SSL) offers a workaround by leveraging unlabeled and labeled data to infer labels for the test
set given a small portion of label information. In this paper we propose a novel algorithm for interactive segmentation
using transductive learning and inference in conditional mixture naïve Bayes models (T-CMNB) with spatial
regularization constraints. T-CMNB is an extension of the transductive naïve Bayes algorithm [1, 20]. The multimodal
Gaussian mixture assumption on the class-conditional likelihood and spatial regularization constraints allow us to
explain more complex distributions required for spatial classification in multimodal imagery. To simplify the estimation
we reduce the parameter space by assuming naïve conditional independence between the feature space and the class
label. The naïve conditional independence assumption allows efficient inference of marginal and conditional
distributions for large scale learning and inference [19]. We evaluate the proposed algorithm on multimodal MRI brain
imagery using ROC statistics and provide preliminary results. The algorithm shows promising segmentation
performance with a sensitivity and specificity of 90.37% and 99.74% respectively and compares competitively to
alternative interactive segmentation schemes.
This work aims at developing innovative algorithms and tools for summarizing echocardiogram videos. Specifically, we summarize the digital echocardiogram videos by temporally segmenting them into the constituent views and representing each view by the most informative frame. For the segmentation we take advantage of the well-defined spatio- temporal structure of the echocardiogram videos. Two different criteria are used: presence/absence of color and the shape of the region of interest (ROI) in each frame of the video. The change in the ROI is due to different modes of echocardiograms present in one study. The representative frame is defined to be the frame corresponding to the end- diastole of the heart cycle. To locate the end-diastole we track the ECG of each frame to find the exact time the time- marker on the ECG crosses the peak of the end-diastole we track the ECG of each frame to find the exact time the time- marker on the ECG crosses the peak of the R-wave. The corresponding frame is chosen to be the key-frame. The entire echocardiogram video can be summarized into either a static summary, which is a storyboard type of summary and a dynamic summary, which is a concatenation of the selected segments of the echocardiogram video. To the best of our knowledge, this if the first automated system for summarizing the echocardiogram videos base don visual content.
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