Computer Assisted Cytological Medical Image Analysis

这项拟议的研究利用模式识别、图像处理、计算几何和机器学习方面的进展来开发新的解决方案，以解决医学成像领域的问题。具体地说，我正在进一步研究用于细胞学诊断的计算机辅助医学诊断、乳腺癌细针活检的细胞学成像以及细血涂片的福尔马里亚检测。对于这些类型的细胞学图像，我的长期目标是开发健壮、可访问、快速和准确的医疗诊断工具，以向病理学家、临床医生和医学专家提供自动独立的第二意见，帮助避免错误/疏忽，或者充当专家系统来帮助非专家医生。计算机辅助医疗诊断或恶性肿瘤分级的过程可以描述为两个主要阶段：从医学图像中提取特征和针对特定医疗问题对图像进行分类。拟议工作的一个目标是确定特定类型的细胞学图像的改进特征集。许多用于医学图像分类问题的常用特征在确定这些特征之前都需要精确的图像分割。因此，我正在研究通过扩展我之前的工作来提高细胞学图像中细胞分割的准确性和速度的方法，从使用Hough变换到使用活动轮廓模型，再到结合有效地使用纹理、每像素分类器和形状建模。我们还将这项研究扩展到新的医学成像技术的准确分割上，如超大型全幻灯片虚拟图像、3D图像和扩展焦点成像(EFI)创建的图像。本研究的另一个目标是提高乳腺癌细针活检细胞学图像的对准分类的准确性。具体地说，我们研究恶性诊断问题，以确定幻灯片是恶性还是良性，以及恶性程度分级，其中我们使用Bloom-Richardson恶性程度分级。为了做到这一点，通过优化的特征集，我们将开发分类器系统来提高敏感率，特别是在假阴性率最低的情况下。由于分类器必须在临床环境中工作，我们将使分类器进行自动参数调整。拟议研究的第三个目标是改进血涂片细胞学图像中疟疾寄生虫的检测。在我们以前完整的血液计数框架的基础上，我们将开发一个计算机辅助疟疾检测系统，以确定疟疾的存在，并在厚血涂片中根据红细胞计数返回准确的感染人数；并在薄血涂片中区分四种疟疾寄生虫。我们将开发具有更高敏感率的分类器系统。我在健康信息学方面的工作有助于医学成像的更大趋势，即开发计算机辅助医疗诊断工具，以补充和促进临床医生和病理学家的工作。所有的开发都是为了使用普遍可用的显微镜和计算资源。例如，在疟疾肆虐的国家的农村地区，往往没有专家对血片甲型疟原虫进行显微镜筛查。通过对这些玻片的自动分析，我的研究中提出的计算机辅助专家系统将有助于填补拥有必要专业知识的专家与实际检查细胞学玻片的医生之间的知识鸿沟，从而实现更好的病人护理。

The proposed research leverages advances in Pattern Recognition, ImageProcessing, Computational Geometry and Machine Learning to develop novelsolutions to address problems in the area of Medical Imaging. Specifically, Iam furthering my research on Computer Aided Medical Diagnosis for cytologicalimagery of breast cancer fine needle biopsies, and of thin blood smears formalaria detection. For these types of cytological images, my long termobjective is to develop robust, accessible, rapid, and accurate MedicalDiagnosis tools to provide an automatic independent second opinion, to helpavoid 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 canbe described in two main stages: feature extraction from medical imagery andclassification of the imagery with respect to specific medical issues. Oneobjective of the proposed work is the determination of improved feature setsfor specific types of cytological images. Many of the frequently usedfeatures used for medical image classification problems require accurateimage segmentation prior to determining these features. Therefore, I amstudying approaches to improve the accuracy and speed of the segmentation ofcells in cytological images by extending my previous work ranging from theuse of the Hough transform to using active contours models, to incorporateeffective use of texture, per-pixel classifiers, and shape modeling. We arealso extending this research to the accurate segmentation of new medicalimaging 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 themalignancy classification in cytological images of breast cancer fine needlebiopsies. Specifically, we study the problems of malignancy diagnosis todetermine whether the slide is malignant or benign, and of malignancy gradingwhere we use the Bloom-Richardson malignancy grading. To accomplish this,with optimized feature sets, we will develop systems of classifiers thatimprove sensitivity rates, particularly with minimal rates of falsenegatives. Since the classifiers have to work in a clinical setting, we willadapt the classifiers to do automatic parameter tuning. The third objectiveof the proposed research is to improve the detection of malaria parasites incytological images of blood smear slides. Building on our previous completeblood count framework, we will develop a computer-assisted malaria detectionsystem to determine the presence of malaria and return accurate infectioncounts per RBC counts in thick blood smears; and to differentiate between thefour species of the malarial parasite in thin blood smears. We will developsystems of classifiers with improved sensitivity rates.My work in Health Informatics contributes to the larger trend in medicalimagery to develop computer assisted medical diagnosis tools to complementand facilitate the work of Clinicians and Pathologists. All developments aredesigned to use commonly available microscopy and computing resources. Forexample, in the rural areas of malaria stricken countries there arefrequently no specialists to do microscopic screening of blood slides formalaria. Through the automatic analysis of these slides, the computerassisted expert systems proposed in my research would help fill the knowledgegap between a specialist with the necessary expertise and the practitioneractually examining cytological slides, thus leading to better patient care.