Yuchen Qiu

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BACKGROUND Auto-focusing is an important operation in high throughput imaging scanning. Although many auto-focusing methods have been developed and tested for a variety of imaging modalities, few investigations have been performed on the selection of an optimal auto-focusing method that is suitable for the pathological metaphase chromosome analysis under a(More)
BACKGROUND The purpose of this study is to identify a set of features for optimizing the performance of metaphase chromosome detection under high throughput scanning microscopy. In the development of computer-aided detection (CAD) scheme, feature selection is critically important, as it directly determines the accuracy of the scheme. Although many features(More)
In digital pathology, clinical specimen slides are converted into digital images by microscopic image scanners. Since random vibration and mechanical drifting are unavoidable on even high-precision moving stages, the optical depth of field (DOF) of microscopic systems may affect image quality, in particular when using an objective lens with high(More)
BACKGROUND In current clinical trials of treating ovarian cancer patients, how to accurately predict patients' response to the chemotherapy at an early stage remains an important and unsolved challenge. PURPOSE To investigate feasibility of applying a new quantitative image analysis method for predicting early response of ovarian cancer patients to(More)
Although Response Evaluation Criteria in Solid Tumors (RECIST) is the current clinical guideline to assess size change of solid tumors after therapeutic treatment, it has a relatively lower association to the clinical outcome of progression free survival (PFS) of the patients. In this paper, we presented a new approach to assess responses of ovarian cancer(More)
OBJECTIVE This study aims to develop a new quantitative image feature analysis scheme and investigate its role along with two genomic biomarkers, namely protein expression of the excision repair cross-complementing 1 genes and a regulatory subunit of ribonucleotide reductase (RRM1), in predicting cancer recurrence risk of stage I nonsmall-cell lung cancer(More)
This paper presents new formulas to determine the depth of field (DOF) of optical and digital microscope systems. Unlike the conventional DOF formula, the new methods consider the interplay of geometric and diffraction optics for infinite and finite optical microscopes and for corresponding digital microscope systems. It is shown that in addition to the(More)
BACKGROUND To investigate the feasibility of automated segmentation of visceral and subcutaneous fat areas from computed tomography (CT) images of ovarian cancer patients and applying the computed adiposity-related image features to predict chemotherapy outcome. METHODS A computerized image processing scheme was developed to segment visceral and(More)
PURPOSE To develop a new computer-aided diagnosis (CAD) scheme using a content-based image retrieval (CBIR) approach for classification between the malignant and benign breast lesions depicted on the digital mammograms and assess CAD performance and reproducibility. METHODS An image dataset including 820 regions of interest (ROIs) was used. Among them,(More)
Accurately assessment of adipose tissue volume inside a human body plays an important role in predicting disease or cancer risk, diagnosis and prognosis. In order to overcome limitation of using only one subjectively selected CT image slice to estimate size of fat areas, this study aims to develop and test a computer-aided detection (CAD) scheme based on(More)