Parallel approach for tubule grading in breast cancer lesions

H.D. Cheng, X.O. Li, D. Riordan, J.N. Scrimger, A. Folye, M.A. Macaulay
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引用次数: 3

Abstract

Sequential algorithms and conventional architectures for grading tubules in digitized images of microscopic slide information of breast cancer malignancies are time-consuming and labor-intensive because of high computational complexity. This paper presents a parallel algorithm and its corresponding VLSI architecture for grading tubules in breast cancer lesions in real time. The algorithm relies on a combination of criteria used by physicians including brightness contrast, dark edge, and uniform density of tubule areas. The algorithm exploits concurrencies in both the space domain and the time domain. It only requires each processing element (PE) to perform three simple operations such as addition, subtraction, and comparison, which makes every PE simple. The essential parallelism in the proposed algorithm facilitates its implementation using VLSI architecture, so that the time complexity is only O(N2/k) if using k × k processing elements, where N is the dimension of the image plane. If k = N, the time complexity will be O(N), compared with O(N2) using a uniprocessor. A series of experiments and simulations have verified the high performance of the proposed algorithm and the parallel VLSI architecture.

乳腺癌病变小管分级的平行方法
由于计算复杂度高,对乳腺癌显微切片信息数字化图像中的小管进行分级的顺序算法和传统架构耗时耗力。本文提出了一种用于乳腺癌病灶小管实时分级的并行算法及其相应的VLSI结构。该算法依赖于医生使用的标准组合,包括亮度对比度、暗边缘和小管区域的均匀密度。该算法利用了空间域和时间域的并发性。它只要求每个处理元素(PE)执行加法、减法、比较等三种简单的操作,使得每个PE都很简单。该算法本质上的并行性有利于在VLSI架构下实现,当使用k × k个处理元素(N为图像平面的维数)时,时间复杂度仅为O(N2/k)。如果k = N,则时间复杂度为O(N),而使用单处理器时为O(N2)。一系列的实验和仿真验证了该算法的高性能和并行VLSI架构。
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