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With continuous advancement of VLSI technology it has become possible to achieve any desired performance metric, but at a cost of increased system complexity. In this paper we present area optimal integer 2-D DCT architecture for H.264/AVC codecs. The 2-D DCT calculation is performed by utilizing the separability property, in such a way, 2-D DCT is divided into two 1-D DCT calculation that share a common memory, which considerably reduces the gate count. Due to its area optimized approach the design will find application in hand-held/mobile devices. The transform module has been coded in Verilog hardware description language (HDL) and synthesized in 0.18µ TSMC technology.

Using more than one classification stage and exploiting class population imbalance allows for incorporating powerful classifiers in tasks requiring large scale training data, even if these classifiers scale badly with the number of training samples. This led us to propose a two-stage classifier for segmenting tibial cartilage in knee MRI scans combining nearest neighbor classification and support vector machines (SVMs). Here we apply it to femoral cartilage segmentation. We describe the similarities and differences between segmenting these two knee cartilages. For further speeding up batch SVM training, we propose loosening the stopping condition in the quadratic program solver before considering moving on to other approximation techniques such as online SVMs. The two-stage approach reached a higher accuracy in comparison to the one-stage state-of-the-art method. It also achieved better inter-scan segmentation reproducibility when compared to a radiologist as well as the current state-of-the-art method.

Many classification/segmentation tasks in medical imaging are particularly challenging for machine learning algorithms because of the huge amount of training data required to cover biological variability. Learning methods scaling badly in the number of training data points may not be applicable. This may exclude powerful classifiers with good generalization performance such as standard non-linear support vector machines (SVMs). Further, many medical imaging problems have highly imbalanced class populations, because the object to be segmented has only few pixels/voxels compared to the background. This article presents a two-stage classifier for large-scale medical imaging problems. In the first stage, a classifier that is easily trainable on large data sets is employed. The class imbalance is exploited and the classifier is adjusted to correctly detect background with a very high accuracy. Only the comparatively few data points not identified as background are passed to the second stage. Here a powerful classifier with high training time complexity can be employed for making the final decision whether a data point belongs to the object or not. We applied our method to the problem of automatically segmenting tibial articular cartilage from knee MRI scans. We show that by using nearest neighbor (kNN) in the first stage we can reduce the amount of data for training a non-linear SVM in the second stage. The cascaded system achieves better results than the state-of-the-art method relying on a single kNN classifier.

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We present a learning based method for image super resolution problem. Our approach uses kernel methods to build an efficient representation and also to learn the regression model. For constructing an efficient set of features, we apply Kernel Principal Component Analysis (Kernel-PCA) with a Gaussian kernel on a patch based database constructed from 69 training images up-scaled using bi-cubic interpolation. These features were given as input to a non-linear Support Vector Regression (SVR) model, with Gaussian kernel, to predict the pixels of the high resolution image. The model selection for SVR was performed using grid search. We tested our algorithm on an unseen data-set of 13 images. Our method out-performed a state-of the-art method and achieved an average of 0.92 dB higher Peak signal-to-noise ratio (PSNR). The average improvement in PSNR over bi-cubic interpolation was found to be 3.38 dB.