We propose a shape model fitting algorithm that uses linear programming optimization. Most shape model fitting approaches (such as ASM, AAM) are based on gradient-descent-like local search optimization and usually suffer from local minima. In contrast, linear programming (LP) techniques achieve globally optimal solution for linear problems. In [1], a linear programming scheme based on successive convexification was proposed for matching static object shape in images among cluttered background and achieved very good performance. In this paper, we rigorously derive the linear formulation of the shape model fitting problem in the LP scheme and propose an LP shape model fitting algorithm (LPSM). In the experiments, we compared the performance of our LPSM with the LP graph matching algorithm(LPGM), ASM, and a CONDENSATION based ASM algorithm on a test set of PUT database. The experiments show that LPSM can achieve higher shape fitting accuracy. We also evaluated its performance on the fitting of some real world face images collected from internet. The results show that LPSM can handle various appearance outliers and can avoid local minima problem very well, as the fitting is carried out by LP optimization with l1norm robust cost function.

This paper describes a new, physically interpretable, fully automatic algorithm for removal of tissue autofluorescence (AF) from fluorescence microscopy images, by non-negative matrix factorization. Measurement of signal intensities from the concentration of certain fluorescent reporter molecules at each location within a sample of biological tissue is confounded by fluorescence produced by the tissue itself (autofluorescence). Spectral mixing models use mixing coefficients to specify how much fluorescence from each source is present and unmixing algorithms separate the two fluorescent sources. Current spectral unmixing methods for AF removal often require a priori knowledge of mixing coefficients. Those which do not, such as principal component analysis, generate negative mixing coefficients that are not physically meaningful. Non-negative matrix factorization constrains mixing coefficients to be non-negative, and has been used for spectral unmixing, but not AF removal. This paper describes a novel non-negative matrix factorization algorithm which separates fluorescent images into true signal and AF components utilizing an estimate of the dark current. We also present a test-bed, based on fluorescent beads, to compare the performance of different AF removal algorithms. Our algorithm out-performed previous state of the art on validation images.

Quantifying expression levels of proteins with sub cellular resolution is critical to many applications ranging from biomarker discovery to treatment planning. In this paper, we present a fully automated method and a new metric that quantifies the expression of target proteins in immunohisto-chemically stained tissue microarray (TMA) samples. The proposed metric is superior to existing intensity or ratio-based methods. We compared performance with the majority decision of a group of 19 observers scoring estrogen receptor (ER) status, achieving a detection rate of 96% with 90% specificity. The presented methods will accelerate the processes of biomarker discovery and transitioning of biomarkers from research bench to clinical utility.