Stain deconvolution using statistical analysis of multi-resolution stain colour representation

Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners

Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation

In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations

Development of methods for capillary isoelectric focusing of dairy proteins : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Chemistry at Massey University

Capillary Isoelectric Focusing (CIEF) is a high-resolution technique which can be applied to the separation and characterisation of complex biological mixtures such as dairy proteins. Although dairy proteins are commonly analysed by traditional gel electrophoresis techniques including 2-Dimensional PAGE, CIEF offers the advantages of reduced analysis times, the ability to handle smaller sample volumes and increased sensitivity with improved separation efficiencies. Several methods for capillary isoelectric focusing of dairy proteins have been developed herein. For the analysis of soluble whey proteins methods that can be used with either UV or mass spectrometry (MS) detection have been set up. For MS detection a coaxial sheath flow interface in conjunction with electrospray ionisation has been utilised. For analysis of the inherently insoluble casein proteins with UV detection denaturing and reducing agents have been introduced into the system. Results have shown very close similarities to those obtained by IEF gels

Towards many colors in FISH on 3D-preserved interphase nuclei

The article reviews the existing methods of multicolor FISH on nuclear targets, first of all, interphase chromosomes. FISH proper and image acquisition are considered as two related components of a single process. We discuss (1) M-FISH (combinatorial labeling + deconvolution + widefield microscopy); (2) multicolor labeling + SIM (structured illumination microscopy); (3) the standard approach to multicolor FISH + CLSM (confocal laser scanning microscopy; one fluorochrome - one color channel); (4) combinatorial labeling + CLSM; (5) non-combinatorial labeling + CLSM + linear unmixing. Two related issues, deconvolution of images acquired with CLSM and correction of data for chromatic Z-shift, are also discussed. All methods are illustrated with practical examples. Finally, several rules of thumb helping to choose an optimal labeling + microscopy combination for the planned experiment are suggested. Copyright (c) 2006 S. Karger AG, Basel

Automated segmentation of tissue images for computerized IHC analysis

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie

Automated Discrimination of Pathological Regions in Tissue Images: Unsupervised Clustering vs Supervised SVM Classification

Recognizing and isolating cancerous cells from non pathological tissue areas (e.g. connective stroma) is crucial for fast and objective immunohistochemical analysis of tissue images. This operation allows the further application of fully-automated techniques for quantitative evaluation of protein activity, since it avoids the necessity of a preventive manual selection of the representative pathological areas in the image, as well as of taking pictures only in the pure-cancerous portions of the tissue. In this paper we present a fully-automated method based on unsupervised clustering that performs tissue segmentations highly comparable with those provided by a skilled operator, achieving on average an accuracy of 90%. Experimental results on a heterogeneous dataset of immunohistochemical lung cancer tissue images demonstrate that our proposed unsupervised approach overcomes the accuracy of a theoretically superior supervised method such as Support Vector Machine (SVM) by 8%

Detection of early osteogenic commitment in primary cells using Raman spectroscopy

Major challenges in the development of novel implant surfaces for artificial joints include osteoblast heterogeneity and the lack of a simple and sensitive in vitro assay to measure early osteogenic responses. Raman spectroscopy is a label-free, non-invasive and non-destructive vibrational fingerprinting optical technique that is increasingly being applied to detect biochemical changes in cells. In this study Raman spectroscopy has been used to obtain bone cell-specific spectral signatures and to identify any changes therein during osteoblast commitment and differentiation of primary cells in culture. Murine calvarial osteoblasts (COBs) were extracted and cultured and studied by Raman spectroscopy over a 14 day culture period. Distinct osteogenic Raman spectra were identified after 3 days of culture with strong bands detected for mineral: phosphate ν3 (1030 cm−1) and B-type carbonate (1072 cm−1), DNA (782 cm−1) and collagen matrix (CH2 deformation at 1450 cm−1) and weaker phosphate bands (948 and 970 cm−1). Early changes were detected by Raman spectroscopy compared to a standard enzymatic alkaline phosphatase (ALP) assay and gene expression analyses over this period. Proliferation of COBs was confirmed by fluorescence intensity measurements using the Picogreen dsDNA reagent. Changes in ALP levels were evident only after 14 days of culture and mRNA expression levels for ALP, Col1a1 and Sclerostin remained constant during the culture period. Sirius red staining for collagen deposition also revealed little change until day 14. In contrast Raman spectroscopy revealed the presence of amorphous calcium phosphate (945–952 cm−1) and carbonated apatite (957–962 cm−1) after only 3 days in culture and octacalcium phosphate (970 cm−1) considered a transient mineral phase, was detected after 5 days of COBs culture. PCA analysis confirmed clear separation between time-points. This study highlights the potential of Raman spectroscopy to be utilised for the early and specific detection of proliferation and differentiation changes in primary cultures of bone cells

Automated Segmentation of Cells with IHC Membrane Staining

This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysi