Transcending Grids: Point Clouds and Surface Representations Powering Neurological Processing

• URL: http://arxiv.org/abs/2305.15426v2
• Date: Fri, 2 Jun 2023 19:09:40 GMT
• Title: Transcending Grids: Point Clouds and Surface Representations Powering Neurological Processing
• Authors: Kishore Babu Nampalle, Pradeep Singh, Vivek Narayan Uppala, Sumit Gangwar, Rajesh Singh Negi, Balasubramanian Raman
• Abstract summary: In healthcare, accurately classifying medical images is vital, but conventional methods often hinge on medical data with a consistent grid structure. Recent medical research has been focused on tweaking the architectures to attain better performance without giving due consideration to the representation of data. We present a novel approach for transforming grid based data into its higher dimensional representations, leveraging unstructured point cloud data structures.
• Score: 13.124650851374316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In healthcare, accurately classifying medical images is vital, but conventional methods often hinge on medical data with a consistent grid structure, which may restrict their overall performance. Recent medical research has been focused on tweaking the architectures to attain better performance without giving due consideration to the representation of data. In this paper, we present a novel approach for transforming grid based data into its higher dimensional representations, leveraging unstructured point cloud data structures. We first generate a sparse point cloud from an image by integrating pixel color information as spatial coordinates. Next, we construct a hypersurface composed of points based on the image dimensions, with each smooth section within this hypersurface symbolizing a specific pixel location. Polygonal face construction is achieved using an adjacency tensor. Finally, a dense point cloud is generated by densely sampling the constructed hypersurface, with a focus on regions of higher detail. The effectiveness of our approach is demonstrated on a publicly accessible brain tumor dataset, achieving significant improvements over existing classification techniques. This methodology allows the extraction of intricate details from the original image, opening up new possibilities for advanced image analysis and processing tasks.

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