Computer Assisted Cytological Medical Image Analysis

这项拟议的研究利用了模式识别、图像 加工、计算几何和机器学习开发新技术 解决医学影像领域的问题的解决方案。具体来说，我 我正在进一步研究细胞学的计算机辅助医学诊断 乳癌细针活检图像和细血涂片图像 疟疾检测。对于这些类型的细胞学图像，我的长期 目标是发展强大、可访问、快速和准确的医疗 诊断工具能够自动提供独立的第二意见，以帮助 避免错误/疏忽，给病理学家、临床医生和医学专家， 或作为专家系统来帮助非专科医生。 计算机辅助医疗诊断或恶性肿瘤分级的过程可以 主要分为两个阶段：从医学图像中提取特征和 与特定医疗问题相关的图像分类。一 拟议工作的目标是确定改进的特征集 用于特定类型的细胞学图像。许多常用的 用于医学图像分类问题的特征要求准确 在确定这些特征之前，先进行图像分割。因此，我是 研究提高车牌图像分割精度和速度的方法 通过扩展我以前的工作，从细胞学图像中的细胞 使用Hough变换来使用活动轮廓模型，以合并 有效使用纹理、每像素分类器和形状建模。我们是 并将这一研究扩展到新医学的准确分割 成像技术，例如非常大的全幻灯片虚拟图像、3D图像、 以及使用扩展焦点成像(EFI)创建的图像。 拟议研究的另一个目标是提高 乳腺癌细针细胞学图像中的恶性分类 活组织检查。具体地说，我们研究了恶性肿瘤诊断问题，以 确定幻灯片是恶性的还是良性的，以及恶性程度 我们使用的是布鲁姆-理查森恶性程度分级。要做到这一点， 有了优化的特征集，我们将开发分类器系统， 提高敏感率，特别是在误判率最低的情况下 底片。由于分类器必须在临床环境中工作，我们将 调整分类器以进行自动参数调整。第三个目标 拟议的研究的一部分是改进疟疾寄生虫的检测 血涂片的细胞学图像。在我们之前完成的基础上构建 血细胞计数框架，我们将开发一种计算机辅助检测疟疾 确定疟疾存在并返回准确感染的系统 厚血涂片中的每红细胞计数；并区分 四种疟疾寄生虫在薄血涂片中。我们将发展 具有改进的敏感率的分类器系统。 我在健康信息学方面的工作促进了医疗领域的大趋势 图像开发计算机辅助医疗诊断工具以补充 并方便临床医生和病理学家的工作。所有的进展都是 旨在使用常用的显微镜和计算资源。为 例如，在疟疾肆虐国家的农村地区， 经常没有专家对血液玻片进行显微筛查 疟疾。通过对这些幻灯片的自动分析，计算机 在我的研究中提出的辅助专家系统将有助于填补知识 拥有必要专业知识的专家和从业者之间的差距 实际检查细胞学玻片，从而带来更好的病人护理。

The proposed research leverages advances in Pattern Recognition, Image Processing, Computational Geometry and Machine Learning to develop novel solutions to address problems in the area of Medical Imaging. Specifically, I am furthering my research on Computer Aided Medical Diagnosis for cytological imagery of breast cancer fine needle biopsies, and of thin blood smears for malaria detection. For these types of cytological images, my long term objective is to develop robust, accessible, rapid, and accurate Medical Diagnosis tools to provide an automatic independent second opinion, to help avoid errors/oversights, to Pathologists, Clinicians and Medical Specialists, or act as an expert system to aid the non-specialist medical practitioner. The process of computer-assisted medical diagnosis or malignancy grading can be described in two main stages: feature extraction from medical imagery and classification of the imagery with respect to specific medical issues. One objective of the proposed work is the determination of improved feature sets for specific types of cytological images. Many of the frequently used features used for medical image classification problems require accurate image segmentation prior to determining these features. Therefore, I am studying approaches to improve the accuracy and speed of the segmentation of cells in cytological images by extending my previous work ranging from the use of the Hough transform to using active contours models, to incorporate effective use of texture, per-pixel classifiers, and shape modeling. We are also extending this research to the accurate segmentation of new medical imaging technologies such as very large full slide virtual images, 3D images, and images created with extended focal imaging (EFI). Another objective of the proposed research is to improve the accuracy of the malignancy classification in cytological images of breast cancer fine needle biopsies. Specifically, we study the problems of malignancy diagnosis to determine whether the slide is malignant or benign, and of malignancy grading where we use the Bloom-Richardson malignancy grading. To accomplish this, with optimized feature sets, we will develop systems of classifiers that improve sensitivity rates, particularly with minimal rates of false negatives. Since the classifiers have to work in a clinical setting, we will adapt the classifiers to do automatic parameter tuning. The third objective of the proposed research is to improve the detection of malaria parasites in cytological images of blood smear slides. Building on our previous complete blood count framework, we will develop a computer-assisted malaria detection system to determine the presence of malaria and return accurate infection counts per RBC counts in thick blood smears; and to differentiate between the four species of the malarial parasite in thin blood smears. We will develop systems of classifiers with improved sensitivity rates. My work in Health Informatics contributes to the larger trend in medical imagery to develop computer assisted medical diagnosis tools to complement and facilitate the work of Clinicians and Pathologists. All developments are designed to use commonly available microscopy and computing resources. For example, in the rural areas of malaria stricken countries there are frequently no specialists to do microscopic screening of blood slides for malaria. Through the automatic analysis of these slides, the computer assisted expert systems proposed in my research would help fill the knowledge gap between a specialist with the necessary expertise and the practitioner actually examining cytological slides, thus leading to better patient care.