Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input

Many high-performance classification models utilize complex CNN-based architectures for Alzheimer's Disease classification. We aim to investigate two relevant questions regarding classification of Alzheimer's Disease using MRI: "Do Vision Transformer-based models perform better than CNN-based models?" and "Is it possible to use a shallow 3D CNN-based model to obtain satisfying results?" To achieve these goals, we propose two models that can take in and process 3D MRI scans: Convolutional Voxel Vision Transformer (CVVT) architecture, and ConvNet3D-4, a shallow 4-block 3D CNN-based model. Our results indicate that the shallow 3D CNN-based models are sufficient to achieve good classification results for Alzheimer's Disease using MRI scans

Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data

Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified. In contrast, in medical imaging, not all parts of the image may be equally significant or relevant to the image retrieval application at hand. For instance, in lung x-ray image, the lung region may contain a tumour, hence being highly significant whereas the surrounding area does not contain significant information from medical diagnosis perspective. In this paper, we propose to detect salient regions of images during training and fold the data to reduce the effect of irrelevant regions. As a result, smaller image areas will be used for LBP features calculation and consequently classification by SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the performance of the proposed approach. The results demonstrate the benefits of saliency-based folding approach that delivers comparable classification accuracies with state-of-the-art but exhibits lower computational cost and storage requirements, factors highly important for big data analytics.Comment: To appear in proceedings of The 14th International Conference on Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA, 201

Autoencoding the Retrieval Relevance of Medical Images

Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015, Orleans, Franc

Computational Redundancy in Image Processing

This research presents a new performance improvement technique, window memoization, for software and hardware implementations of local image processing algorithms. Window memoization combines the memoization techniques proposed in software and hardware with a characteristic of image data, computational redundancy, to improve the performance (in software) and efficiency (in hardware) of local image processing algorithms. The computational redundancy of an image indicates the percentage of computations that can be skipped when performing a local image processing algorithm on the image. Our studies show that computational redundancy is inherited from two principal redundancies in image data: coding redundancy and interpixel redundancy. We have shown mathematically that the amount of coding and interpixel redundancy of an image has a positive effect on the computational redundancy of the image where a higher coding and interpixel redundancy leads to a higher computational redundancy. We have also demonstrated (mathematically and empirically) that the amount of coding and interpixel redundancy of an image has a positive effect on the speedup obtained for the image by window memoization in both software and hardware. Window memoization minimizes the number of redundant computations performed on an image by identifying similar neighborhoods of pixels in the image. It uses a memory, reuse table, to store the results of previously performed computations. When a set of computations has to be performed for the first time, the computations are performed and the corresponding result is stored in the reuse table. When the same set of computations has to be performed again in the future, the previously calculated result is reused and the actual computations are skipped. Implementing the window memoization technique in software speeds up the computations required to complete an image processing task. In software, we have developed an optimized architecture for window memoization and applied it to six image processing algorithms: Canny edge detector, morphological gradient, Kirsch edge detector, Trajkovic corner detector, median filter, and local variance. The typical speedups range from 1.2 to 7.9 with a maximum factor of 40. We have also presented a performance model to predict the speedups obtained by window memoization in software. In hardware, we have developed an optimized architecture that embodies the window memoization technique. Our hardware design for window memoization achieves high speedups with an overhead in hardware area that is significantly less than that of conventional performance improvement techniques. As case studies in hardware, we have applied window memoization to the Kirsch edge detector and median filter. The typical and maximum speedup factors in hardware are 1.6 and 1.8, respectively, with 40% less hardware in comparison to conventional optimization techniques