An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.

Machine Learning Methods for Histopathological Image Analysis.

Visual-assisted guidance of an ultra-thin flexible endoscope to a predetermined region of interest within a lung during a bronchoscopy procedure. The region may be an opacity-identified by non-invasive imaging methods, such as high-resolution computed tomography (HRCT) or as a malignant lung mass that was diagnosed in a previous examination. An embedded position sensor on the flexible endoscope indicates the position of the distal tip of the probe in a Cartesian coordinate system during the procedure. A visual display is continually updated, showing the present position and orientation of the marker in a 3-D graphical airway model generated from image reconstruction. The visual display also includes windows depicting a virtual fly-through perspective and real-time video images acquired at the head of the endoscope, which can be stored as data, with an audio or textual account.

Catheterscope 3D Guidance and Interface System

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Cell Population Tracking and Lineage Construction with Spatiotemporal Context

Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis

Spatio-temporal cell cycle phase analysis using level sets and fast marching methods.

Registration is one of the most common tasks of image analysis and computer vision applications. The requirements of most registration algorithms include large capture range and fast computation so that the algorithms are robust to different scenarios and can be computed in a reasonable amount of time. For these purposes, registration in the Fourier domain using normalized cross-correlation is well suited and has been extensively studied in the literature. Another common requirement is masking, which is necessary for applications where certain regions of the image that would adversely affect the registration result should be ignored. To address these requirements, we have derived a mathematical model that describes an exact form for embedding the masking step fully into the Fourier domain so that all steps of translation registration can be computed efficiently using Fast Fourier Transforms. We provide algorithms and implementation details that demonstrate the correctness of our derivations. We also demonstrate how this masked FFT registration approach can be applied to improve the Fourier-Mellin algorithm that calculates translation, rotation, and scale in the Fourier domain. We demonstrate the computational efficiency, advantages, and correctness of our algorithm on a number of images from real-world applications. Our framework enables fast, global, parameter-free registration of images with masked regions.

Masked Object Registration in the Fourier Domain

Prostate cancer is the most common cancer among men, excluding skin cancer. It is diagnosed by histopathology interpretation of Hematoxylin and Eosin (H&E)-stained tissue sections. Gland and nuclei distributions vary with the disease grade, and the morphological features vary with the advance of cancer. A tissue microarray with known disease stages can be used to enable efficient pathology slide image analysis. We focus on an intuitive approach for segmenting such images, using the Hierarchical Self-Organizing Map (HSOM). Our approach introduces the use of unsupervised clustering using both color and texture features, and the use of unsupervised color merging outside of the HSOM framework. The HSOM was applied to segment 109 tissues composed of four tissue clusters: glands, epithelia, stroma, and nuclei. These segmentations were compared with the results of an EM Gaussian clustering algorithm. The proposed method confirms that the self-learning ability and adaptability of the HSOM, coupled with the information fusion mechanism of the hierarchical network, leads to superior segmentation results for tissue images.

Color and texture based segmentation of molecular pathology images usING HSOMS

Want to get experience? Want to get any ideas to create new things in your life? Read biomedical image analysis now! By reading this book as soon as possible, you can renew the situation to get the inspirations. Yeah, this way will lead you to always think more and more. In this case, this book will be always right for you. When you can observe more about the book, you will know why you need this.

Dirk R. Padfield

Papers

Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis

Spatio-temporal cell cycle phase analysis using level sets and fast marching methods.

Masked Object Registration in the Fourier Domain

Color and texture based segmentation of molecular pathology images usING HSOMS

Biomedical Image Analysis