2022-12-01 更新
Improving Segmentation of Breast Ultrasound Images: Semi Automatic Two Pointers Histogram Splitting Technique
Authors:Rasheed Abid, S. Kaisar Alam
Automatically segmenting lesion area in breast ultrasound (BUS) images is a challenging one due to its noise, speckle and artifacts. Edge-map of BUS images also does not help because in most cases the edge-map gives no information whatsoever. Almost all segmentation technique takes the edge-map of the image as its first step, though there are a few algorithms that try to avoid edge-maps as well. Improving the edge-map of breast ultrasound images theoretically improves the chances of automatic segmentation to be more precise. In this paper, we propose a semi-automatic technique of histogram splitting using two pointers. Here the user only has to select two initially guessed points denoting a circle on the region of interest (ROI). The method will automatically study the internal histogram and split it using two pointers. The output BUS image has improved edge-map and ultimately the segmentation on it is better compared to regular segmentation using same algorithm and same initialization. Also, we further processed the edge-map to have less edge-pixels to area ratio, improving the homogeneity and the chances of easy segmentation in the future.
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A multi-objective constrained POMDP model for breast cancer screening
Authors:Can Kavaklioglu, Mucahit Cevik, Robert Helmeczi, Davood Pirayesh Neghab
Breast cancer is a common and deadly disease, but it is often curable when diagnosed early. While most countries have large-scale screening programs, there is no consensus on a single globally accepted policy for breast cancer screening. The complex nature of the disease; limited availability of screening methods such as mammography, magnetic resonance imaging (MRI), and ultrasound screening; and public health policies all factor into the development of screening policies. Resource availability concerns necessitate the design of policies which conform to a budget, a problem which can be modelled as a constrained partially observable Markov decision process (CPOMDP). In this study, we propose a multi-objective CPOMDP model for breast cancer screening with two objectives: minimize the lifetime risk of dying due to breast cancer and maximize the quality-adjusted life years. Additionally, we consider an expanded action space which allows for screening methods beyond mammography. Each action has a unique impact on quality-adjusted life years and lifetime risk, as well as a unique cost. Our results reveal the Pareto frontier of optimal solutions for average and high risk patients at different budget levels, which can be used by decision makers to set policies in practice.
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