LAW Yan Nei

Robust Modelling and Analysis of Vascular Geometries from Biomedical Images

Published date : 15 Feb 2016

In this paper, a robust computational framework is proposed for the modelling and analysis of vascular geometries from biomedical images. The approach consists of the segmentation of vascular geometries using an active contour model and the extraction of geometric features. A robust image feature is derived based on geometric interactions between the active contour model and the image object boundaries. The derived image feature uses voxel interactions across the image domain, and gives a coherent representation of the vessel shapes in the image.

type
Conference Paper/Poster
journal
Proceedings of the IASTED International Conference in Biomedical Engineering (BioMed) 2016, 832-024. February 2016

Automated Image Based Prominent Nucleoli Detection

Published date : 23 Jun 2015

Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues.

type
Journal Paper
journal
Journal of Pathology Informatics 2015, Vol. 6, Issue 1,doi: 10.4103/2153-3539.159232

Analyzing Cell and Tissure Morphologies Using Pattern Recognition Algorithms

Published date : 13 Feb 2015

This chapter concentrates on the development of biomedical image analysis. It begins with a discussion on three works for detecting objects in tissue and cellular images using image processing approaches. The chapter then presents a texture segmentation model for aiding diagnosis of premalignant endometrial disease. It also presents a method focusing on spot clustering for mutant detection in microscopy images of in vitro cultured keratinocytes. The chapter explains an ellipse detection method for cell and nucleus detection in microscopy image.

type
Book/Book Chapter
journal
Biomedical Image Understanding - Methods and Applications First Edition, 2015, Pg: 113-152, doi: 10.1002/9781118715321.ch4

Automated Breast Tissue Density Assessment Using High Order Regional Texture Descriptors in Mammography

Published date : 15 Feb 2014

Breast cancer is the most common cancer and second leading cause of cancer death among women in the US. The relative survival rate is lower among women with a more advanced stage at diagnosis. Early detection through screening is vital. Mammography is the most widely used and only proven screening method for reliably and effectively detecting abnormal breast tissues. In particular, mammographic density is one of the strongest breast cancer risk factors, after age and gender, and can be used to assess the future risk of disease before individuals become symptomatic.

type
Conference Paper/Poster
journal
Proceedings of SPIE Medical Imaging 2014: Computer-Aided Diagnosis, Vol. 9035, 15-20 Feb 2014, doi:10.1117/12.2043332

Cell tracking using phase-adaptive shape prior

Published date : 20 Aug 2013

Automated tracking of cell population is very crucial for quantitative measurements of dynamic cell-cycle behaviour of individual cells. This problem involves several subproblems and a high accuracy of each step is essential to avoid error propagation. In this paper, we propose a holistic three-component system to tackle this problem. For each phase, we first learn a mean shape as well as a model of the temporal dynamics of transformation, which are used for estimating a shape prior for the cell in the current frame. We then segment the cell using a level set-based shape prior model.

type
Journal Paper
journal
Journal of Microscopy, 2013, doi: 10.1111/jmi.12078

author

Development of MammoQuant: An Automated Quantitative Tool for Standardized Image Analysis of Murine Mammary Gland Morphogenesis

Published date : 01 Dec 2012

The identification of breast cancer genes can benefit highly from studies pertaining to the genetic effects on normal growth and morphogenesis of the mammary gland. Such studies currently lack, but need, standardized, quantitative assessment of relevant developmental features. To address this need, we created two computational frameworks for automated analysis of images of whole-mounted, carmine-stained murine mammary glands.

type
Journal Paper
journal
Journal of Medical Imaging and Health Informatics 2012, Vol. 2, No. 4, Pg 352-365

Level Set based Tracking for Cell Cycle Analysis using Dynamical Shape Prior

Published date : 14 Aug 2012

Automated cell tracking in populations is very crucial for studying dynamic cell cycle behaviors. However, high accuracy of each step is essential to avoid error propagation. In this paper, we propose an integrated three-component system to tackle this problem. We first model the temporal dynamics of shape change using an autoregressive model, which is used for estimating the shape and the location of the current object. We then segment the cell using active contour model starting from the predicted shape. Finally, we identify

type
Conference Paper/Poster
journal
Proceeding of the 16th Conference on Medical Image Understanding and Analysis (MIUA)

A Semisupervised Segmentation Model for Collections of Images

Published date : 11 May 2012

In this paper, we consider the problem of segmentation of large collections of images. We propose a semisupervised optimization model that determines an efficient segmentation of many input images. The advantages of the model are twofold. First, the segmentation is highly controllable by the user so that the user can easily specify what he/she wants. This is done by allowing the user to provide, either offline or interactively, some (fully or partially) labeled pixels in images as strong priors for the model.

type
Journal Paper
journal
IEEE Transactions on Image Processing, June 2012. Vol. 21, Issue 6, Pg 2955-2968, doi : 10.1109/TIP.2012.2187670

Accurate single-molecule localization of superresolution microscopy images using multiscale products

Published date : 13 Feb 2012

Recently, a class of single-molecule based localization techniques such as the Photo-activated Localization Mi- croscopy (PALM) or the Stochastic Optical Reconstruction Microscopy (STORM) has ingeniously brought light- microscopy beyond the diraction limit. However, as the single-molecule images contain point source objects (which have no clear edges, alignment and usually superimposed to the background), traditional restoration techniques used for industrial vision images do not give satisfactory result on the PALM/STORM dataset.

type
Conference Paper/Poster
journal
Proceedings of SPIE Volume 8228 (2012), doi: 10.1117/12.907221