Identifying defects and varieties of Malting Barley Kernels.

This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis.

Measuring dimensionality of cell-scaffold contacts of primary human bone marrow stromal cells cultured on electrospun fiber scaffolds.

The properties and structure of the cellular microenvironment can influence cell behavior. Sites of cell adhesion to the extracellular matrix (ECM) initiate intracellular signaling that directs cell functions such as proliferation, differentiation, and apoptosis. Electrospun fibers mimic the fibrous nature of native ECM proteins and cell culture in fibers affects cell shape and dimensionality, which can drive specific functions, such as the osteogenic differentiation of primary human bone marrow stromal cells (hBMSCs), by.

Orthorectification of Skin Nevi Images by Means of 3D Model of the Human Body.

Melanoma is the most lethal form of skin cancer, and develops from mutation of pigment-producing cells. As it becomes malignant, it usually grows in size, changes proportions, and develops an irregular border. We introduce a system for early detection of such changes, which enables whole-body screening, especially useful in patients with atypical mole syndrome.

Introduction to Radiomics.

Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics-the so-called radiomic features-within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and may, alone or in combination with demographic, histologic, genomic, or proteomic data, be used for clinical problem solving. The goal of this continuing education article is to provide an introduction to the field, covering the basic radiomics workflow: feature calculation and selection, dimensionality reduction, and data processing.

Simulation of phase contrast angiography for renal arterial models.

Background: With the development of versatile magnetic resonance acquisition techniques there arises a need for more advanced imaging simulation tools to enable adequate image appearance prediction, measurement sequence design and testing thereof. Recently, there is a growing interest in phase contrast angiography (PCA) sequence due to the capabilities of blood flow quantification that it offers. Moreover, as it is a non-contrast enhanced protocol, it has become an attractive option in areas, where usage of invasive contrast agents is not indifferent for the imaged tissue.

Modeling, validation and verification of three-dimensional cell-scaffold contacts from terabyte-sized images.

Background: Cell-scaffold contact measurements are derived from pairs of co-registered volumetric fluorescent confocal laser scanning microscopy (CLSM) images (z-stacks) of stained cells and three types of scaffolds (i.e., spun coat, large microfiber, and medium microfiber).

Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms.

Background And Objective: Accurate vessel segmentation of magnetic resonance angiography (MRA) images is essential for computer-aided diagnosis of cerebrovascular diseases such as stenosis or aneurysm. The ability of a segmentation algorithm to correctly reproduce the geometry of the arterial system should be expressed quantitatively and observer-independently to ensure objectivism of the evaluation.

Methods: This paper introduces a methodology for validating vessel segmentation algorithms using a custom-designed MRA simulation framework.

Computer simulation of magnetic resonance angiography imaging: model description and validation.

With the development of medical imaging modalities and image processing algorithms, there arises a need for methods of their comprehensive quantitative evaluation. In particular, this concerns the algorithms for vessel tracking and segmentation in magnetic resonance angiography images. The problem can be approached by using synthetic images, where true geometry of vessels is known.

Texture and color based image segmentation and pathology detection in capsule endoscopy videos.

This paper presents an in-depth study of several approaches to exploratory analysis of wireless capsule endoscopy images (WCE). It is demonstrated that versatile texture and color based descriptors of image regions corresponding to various anomalies of the gastrointestinal tract allows their accurate detection of pathologies in a sequence of WCE frames. Moreover, through classification of single pixels described by texture features of their neighborhood, the images can be segmented into homogeneous areas well matched to the image content.

Quantitative analysis of lumbar intervertebral disc abnormalities at 3.0 Tesla: value of T(2) texture features and geometric parameters.

T(2) relaxation time mapping provides information about the biochemical status of intervertebral discs. The present study aimed to determine whether texture features extracted from T(2) maps or geometric parameters are sensitive to the presence of abnormalities at the posterior aspect of lumbar intervertebral discs, i.e.

A model of deformable rings for interpretation of wireless capsule endoscopic videos.

Wireless Capsule Endoscopy (WCE) provides a means to obtain a detailed video of the small intestine. A single session with WCE may produce nearly 8h of video. Its interpretation is tedious task, which requires considerable expertise and is very stressful.

MaZda--a software package for image texture analysis.

MaZda, a software package for 2D and 3D image texture analysis is presented. It provides a complete path for quantitative analysis of image textures, including computation of texture features, procedures for feature selection and extraction, algorithms for data classification, various data visualization and image segmentation tools. Initially, MaZda was aimed at analysis of magnetic resonance image textures.