Novel Approach for Automatic Region of Interest and Seed Point Detection in CT Images Based on Temporal and Spatial Data

• 作者: Charlie Maere Yuqing Song Zhe Liu
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: COMPUTED TOMOGRAPHY IMAGE continuously adaptive MEAN-SHIFT hounsfield particle-size distribution
• 年，卷（期）: 2019,（5）
• 页码范围: 669-686
• 页数: 18页
• 分类号: TP3

摘要

Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing.We propose a novel approach of automatically identifying region of interest in Computed Tomography Image(CT)images based on temporal and spatial data.Our method is a 3 stages approach,1)We extract organ features from the CT images by adopting the Hounsfield filter.2)We use these filtered features and introduce our novel approach of selecting observable feature candidates by calculating contours’area and automatically detect a seed point.3)We use a novel approach to track the growing region changes across the CT image sequence in detecting region of interest,given a seed point as our input.We used quantitative and qualitative analysis to measure the accuracy against the given ground truth and our results presented a better performance than other generic approaches for automatic region of interest detection of organs in abdominal CT images.With the results presented in this research work,our proposed novel sequence approach method has been proven to be superior in terms of accuracy,automation and robustness.