腦部醫學影像特徵計算、檢索及測量雲端服務之研究

[[abstract]]本計畫預計提出「腦部醫學影像特徵計算、檢索及測量雲端服務之研究」，預計以 三年時間，依序完成腦部相關之特徵擷取計算、資料庫檢索及測量之醫療雲端服務項 目。第一年計畫著重在2D 影像分析與處理程序，以影像ROI 偵測與分割技術萃取腦部 組織結構，根據阻塞性腦水腫與腦萎縮之病理特徵，進行腦部組織結構之特徵擷取與計 算，最後以SVM 技術進行特徵的辨識與分類。第二年計畫將收集醫學影像，建構Web Server 伺服器及醫學影像資料庫，並利用第一年計畫所發展之技術，進行醫學影像的特 徵計算，建立基於特徵向量之資料庫，發展快速匹配演算法及快速搜尋引擎模組，建立 以內容為基礎的影像檢索系統。第三年計畫著重在3D 測量技術之研究，以重建醫學影 像3D 立體模型為基礎，發展基於形態測量(morphometry)技術之腦部3D 組織結構測量 方式，發掘出正常與異常腦部結構兩者不同的變化情形，並將變化情形以3D 立體模型 顯示出來，幫助醫師進行臨床的醫學診斷與研究，最後與其他子計畫進行系統整合，完 成「安全的健康醫療雲端服務之建構與研究」。 A study of cloud services for feature computing, retrieval, and measurement in brain medical images is proposed in this project. The project is estimated to be completed in three years, including all the major development tasks such as brain feature computing, image database retrieval, and 3D measurement of brain images. In the first year, we will focus on 2D image analysis and processing issues. Image ROI detection and segmentation techniques will be developed for the extraction of brain tissue structures. Next, features of brain tissue structures based on pathology characteristic of hydrocephalus and atrophy are computed. The features are then used for recognition and classification using SVM (Support Vector Machine) techniques. In the second year, we will collect brain medical images and establish the web server and the brain medical image database. The techniques developed in the first year will be used to extract features from the images and to construct a feature-vector-based database. Next, fast matching method and fast searching engine module will be developed to construct a content-based image retrieval system for brain medical images. In the third year, we will focus on the development of 3D measurement techniques. Morphometry-based 3D brain tissue structures measurement method will be developed using 3D reconstruction of brain medical images. This measurement can be used to discover normal and abnormal brain tissues structures, and to display the differences in a 3D model. Such technique will help physicians in clinical diagnosis and research. Finally, the methods and techniques proposed in this project will be integrated with the techniques proposed in other projects to complete the construction of secure cloud services for healthcare

Automatic thresholding for defect detection

[[abstract]]Automatic thresholding has been widely used in the machine vision industry for automated visual inspection of defects. A commonly used thresholding technique, the Otsu method, provides satisfactory results for thresholding an image with a histogram of bimodal distribution. This method, however, fails if the histogram is unimodal or close to unimodal. For defect detection applications, defects can range from no defect to small or large defects, which means that the gray-level distributions range from unimodal to bimodal. For this paper, we revised the Otsu method for selecting optimal threshold values for both unimodal and bimodal distributions, and tested the performance of the revised method, the valley-emphasis method, on common defect detection applications

Extraction and Analysis of Structural Features of Lateral Ventricle in Brain Medical Images

[[abstract]]Structural characteristics of lateral ventricle in brain medical images from CT scan can be helpful for distinguishing between normal brain structure and abnormal brain structure. This paper presents image processing algorithms for automatic segmentation and measurement of structural features of lateral ventricle in brain medical images. the effectiveness of such features on brain structure discrimination is also examined. Experimental results show that structural features of lateral ventricle in brain medical images, including Frontal Horn Radius and Ventricular Index, are useful for discriminating between normal and abnormal brain structure