Image Analysis & Stereology

  • 3D GRAY-LEVEL HISTOMORPHOMETRY OF TRABECULAR BONE – A METHODOLOGICAL REVIEW
    Zbisław Tabor, Zbigniew Latała
    The goal of standard histomorphometry is to provide methods of qualitative description of tissue structure based on image data. Typical measurements include geometric areas, perimeters, length, angle of orientation, form factors, center of gravity coordinates etc. There are well-established procedures for deriving the aforementioned quantities from binary images. However, segmentation of in vivo images of trabecular bone poses a problem which has not been solved yet. Recent years have brought significant developments within an emerging field of “gray-level histomorphometry”. The general goal of gray-level histomorphometry is to provide procedures for measuring geometric areas, perimeters, length, angle of orientation, form factors, center of gravity coordinates etc. without the need for image segmentation. Although the field is not very mature yet, the collected results suggest that this approach opens new perspectives which should not be overlooked by the scientific community. In the present review we summarize the state-of-the-art within the 3D gray-level histomorphometry.
     
  • A COMPREHENSIVE FRAMEWORK FOR AUTOMATIC DETECTION OF PULMONARY NODULES IN LUNG CT IMAGES
    Mehdi Alilou, Vassili Kovalev, Eduard Snezhko, Vahid Taimouri
    Solitary pulmonary nodules may indicate an early stage of lung cancer. Hence, the early detection of nodules is the most efficient way for saving the lives of patients. The aim of this paper is to present a comprehensive Computer Aided Diagnosis (CADx) framework for detection of the lung nodules in computed tomography images. The four major components of the developed framework are lung segmentation, identification of candidate nodules, classification and visualization. The process starts with segmentation of lung regions from the thorax. Then, inside the segmented lung regions, candidate nodules are identified using an approach based on multiple thresholds followed by morphological opening and 3D region growing algorithm. Finally, a combination of a rule-based procedure and support vector machine classifier (SVM) is utilized to classify the candidate nodules. The proposed CADx method was validated on CT images of 60 patients, containing the total of 211 nodules, selected from the publicly available Lung Image Database Consortium (LIDC) image dataset. Comparing to the other state of the art methods, the proposed framework demonstrated acceptable detection performance (Sensitivity: 0.80; Fp/Scan: 3.9). Furthermore, we visualize a range of anatomical structures including the 3D lung structure and the segmented nodules along with the Maximum Intensity Projection (MIP) volume rendering method that will enable the radiologists to accurately and easily estimate the distance between the lung structures and the nodules which are frequently difficult at best to recognize from CT images.
     
  • NUMERICAL SIMULATION OF AL-SI ALLOYS WITH AND WITHOUT A DIRECTIONAL SOLIDIFICATION
    Michael Roland, Anastasia Kruglova, Nils Harste, Frank Mücklich, Stefan Diebels
    Numerical simulations are presented to analyze the influence of the casting process on the resulting strength of Strontium modified Al–Si alloys. A relationship is identified between the mechanical behavior and the different 3D morphologies of the eutectic silicon of the samples obtained by the die cast procedure and the directional solidification. It is shown that the mechanical behavior of the die cast alloy is isotropic in all three directions. In contrary, for the directional solidified alloy, the mechanical strength in the direction of the temperature gradient is higher than in the transverse direction. This fact has to be taken into account when analyzing structures issued from different casting processes. The volume meshes for the simulations are generated from experimental 3D FIB/SEM data sets. The influence of several levels of coarsening of the meshes as well as the order of the Lagrange element in the finite element setup are also analyzed.
     
  • THE CHARACTER OF PLANAR TESSELLATIONS WHICH ARE NOT SIDE-TO-SIDE
    Richard Cowan, Christoph Thäle
    This paper studies stationary tessellations and tilings of the plane in which all cells are convex polygons. The focus is on the class of tessellations which are not side-to-side. The character of these tessellations is explored, with special attention paid to the relationship between edges of the tessellation and sides of the polygonal cells and to the combinatorial topology between the ‘adjacent’ geometric elements of the tessellation. Three new parameters, e0,e1 and e2 summing to unity, are introduced. These capture the essence of non side-to-side tessellations and play a role in understanding the adjacency of sides and cells. Examples illustrate the theory.
     
  • PLANAR SECTIONS THROUGH THREE-DIMENSIONAL LINE-SEGMENT PROCESSES
    Sascha Djamal Matthes, Dietrich Stoyan
    This paper studies three-dimensional segment processes in the framework of stochastic geometry. The main objective is to find relations between the characteristics of segment processes such as orientation- and length-distribution, and characteristics of their sections with planes. Formulae are derived for the distribution of segment lengths on both sides of the section plane and corresponding orientations, where it is permitted that there are correlations between the angles and lengths of the line-segments.
     
  • UNSUPERVISED DATA AND HISTOGRAM CLUSTERING USING INCLINED PLANES SYSTEM OPTIMIZATION ALGORITHM
    Mohammad Hamed Mozaffari, Seyed Hamid Zahiri
    Within the last decades, clustering has gained significant recognition as one of the data mining methods, especially in the relatively new field of medical engineering for diagnosing cancer. Clustering is used as a database to automatically group items with similar characteristics. Researchers aim to introduce a novel and powerful algorithm known as Inclined Planes system Optimization (IPO), with capacity to overcome clustering problems. The proposed method identifies each agent used in the algorithm to indicate the centroids of the clusters and automatically select the number of centroids in each time interval (unsupervised clustering). The evaluation method for clustering is based on the Davies Bouldin index (DBi) to show cluster validity. Researchers compare known algorithm on series of data bases from various studies to demonstrate the power and capability of the proposed method. These datasets are popular for pattern recognition with diversity in space dimension. Method performance was tested on standard images as a dataset. Study results show significant method advantage over other algorithms. 
     
  • MODELS OF COVARIANCE FUNCTIONS OF GAUSSIAN RANDOM FIELDS ESCAPING FROM ISOTROPY, STATIONARITY AND NON NEGATIVITY
    Pablo Gregori, Emilio Porcu, Jorge Mateu
    This paper represents a survey of recent advances in modeling of space or space-time Gaussian Random Fields (GRF), tools of Geostatistics at hand for the understanding of special cases of noise in image analysis. They can be used when stationarity or isotropy are unrealistic assumptions, or even when negative covariance between some couples of locations are evident. We show some strategies in order to escape from these restrictions, on the basis of rich classes of well known stationary or isotropic non negative covariance models, and through suitable operations, like linear combinations, generalized means, or with particular Fourier transforms.