Automatic identification of angiogenesis in double stained images of liver tissue

AutoTag and AutoSnap: Standardized, semi-automatic capture of regions of interest from whole slide images

Tumor angiogenesis is measured by counting microvessels in tissue sections at high power magnification as a potential prognostic or predictive biomarker. Until now, regions of interest (ROIs) were selected by manual operations within a tumor by using a systematic uniform random sampling (SURS) approach. Although SURS is the most reliable sampling method, it implies a high workload. However, SURS can be semi-automated and in this way contribute to the development of a validated quantification method for microvessel counting in the clinical setting. Here, we report a method to use semi-automated SURS for microvessel counting: •Whole slide imaging with Pannoramic SCAN (3DHISTECH)•Computer-assisted sampling in Pannoramic Viewer (3DHISTECH) extended by two self-written AutoHotkey applications (AutoTag and AutoSnap)•The use of digital grids in Photoshop(®) and Bridge(®) (Adobe Systems) This rapid procedure allows traceability essential for high throughput protein analysis of immunohistochemically stained tissue.

Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis

Color deconvolution has emerged as a popular method for color unmixing as a pre-processing step for image analysis of digital pathology images. One deficiency of this approach is that the stain matrix is pre-defined which requires specific knowledge of the data. This paper presents an unsupervised Sparse Non-negative Matrix Factorization (SNMF) based approach for color unmixing. We evaluate this approach for color unmixing of breast pathology images. Compared to Non-negative Matrix Factorization (NMF), the sparseness constraint imposed on coefficient matrix aims to use more meaningful representation of color components for separating stained colors. In this work SNMF is leveraged for decomposing pure stained color in both Immunohistochemistry (IHC) and Hematoxylin and Eosin (H&E) images. SNMF is compared with Principle Component Analysis (PCA), Independent Component Analysis (ICA), Color Deconvolution (CD), and Non-negative Matrix Factorization (NMF) based approaches. SNMF demonstrated improved performance in decomposing brown diaminobenzidine (DAB) component from 36 IHC images as well as accurately segmenting about 1400 nuclei and 500 lymphocytes from H & E images.

Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images

Background

A reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods.

Results

A new method to extract closely located, small target spots from biological images is proposed. This method starts with a simple but practical operation based on the extended morphological top-hat transformation to subtract an uneven background. The core of our novel approach is the following: first, the original image is rotated in an arbitrary direction and each rotated image is opened with a single straight line-segment structuring element. Second, the opened images are unified and then subtracted from the original image. To evaluate these procedures, model images of simulated spots with closely located targets were created and the efficacy of our method was compared to that of conventional morphological filtering methods. The results showed the better performance of our method. The spots of real microscope images can be quantified to confirm that the method is applicable in a given practice.

Conclusions

Our method achieved effective spot extraction under various image conditions, including aggregated target spots, poor signal-to-noise ratio, and large variations in the background intensity. Furthermore, it has no restrictions with respect to the shape of the extracted spots. The features of our method allow its broad application in biological and biomedical image information analysis.

Computer-aided Image Processing of Angiogenic Histological

This article reviews the questions regarding the image evaluation of angiogeneic histological samples, particularly the ovarian epithelial cancer. Review is focused on the principles of image analysis in the field of histology and pathology. The definition, classification, pathogenesis and angiogenesis regulation in the ovaries are also briefly discussed. It is hoped that the complex image analysis together with the patient's clinical parameters will allow an acquiring of a clear pathogenic picture of the disease, extension of the differential diagnosis and become a useful tool for the evaluation of drug effects. The challenge of the assessment of angiogenesis activity is the heterogeneity of several objects: parameters derived from patient's anamnesis as well as of pathology samples. The other unresolved problems are the subjectivity of the region of interest selection and performance of the whole slide scanning.

KEYWORDS

Angiogenesis; Image processing; Microvessel density; Cancer; Pathology.