The nature of statistical learning theory
The nature of statistical learning theory
International Journal of Computer Vision
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Real-Time Elastic Deformations of Soft Tissues for Surgery Simulation
IEEE Transactions on Visualization and Computer Graphics
Filling-in by joint interpolation of vector fields and gray levels
IEEE Transactions on Image Processing
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
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This paper presents a learning-based approach to the problem of segmentation of laparoscopic images. The first step of the proposed method is to preprocess input images with a homomorphic filter. An initial segmentation map is then computed using a region growing based image segmentation algorithm. The obtained regions are finally classified using a support vector machine (SVM) to produce the final segmentation. The preliminary results computed on two image sets were promising. The first set includes laparoscopic imugvs recorded in a controlled environment. The second set includes laparoscopic images recorded during three disk removal surgeries performed laparoscopically at Sainte-Justine Hospital.