- THE INVARIATOR DESIGN: AN UPDATE
Luis Manuel Cruz-Orive, Ximo Gual-Arnau
The invariator is a method to generate a test line within an isotropically oriented plane through a fixed point, in such a way that the test line is effectively motion invariant in three dimensional space. Generalizations exist for non Euclidean spaces. The invariator design is convenient to estimate surface area and volume simultaneously. In recent years a number of new results have appeared which call for an updated survey. We include two new estimators, namely the a posteriori weighting estimator for surface area and volume, and the peak-and-valley formula for surface area.
- SHAPE FROM TEXTURE USING LOCALLY SCALED POINT PROCESSES
Eva-Maria Didden, Thordis Thorarinsdottir, Alex Lenkoski, Christoph Schnörr
Shape from texture refers to the extraction of 3D information from 2D images with irregular texture. This paper introduces a statistical framework to learn shape from texture where convex texture elements in a 2D image are represented through a point process. In a first step, the 2D image is preprocessed to generate a probability map corresponding to an estimate of the unnormalized intensity of the latent point process underlying the texture elements. The latent point process is subsequently inferred from the probability map in a non-parametric, model free manner. Finally, the 3D information is extracted from the point pattern by applying a locally scaled point process model where the local scaling function represents the deformation caused by the projection of a 3D surface onto a 2D image.
- GEOMETRIC ANALYSIS OF PLANAR SHAPES WITH APPLICATIONS TO CELL DEFORMATIONS
Ximo Gual-Arnau, Silena Herold-García, Amelia Simó
Shape analysis is of great importance in many fields such as computer vision, medical imaging, and computational biology. In this paper we focus on a shape space in which shapes are represented by means of planar closed curves. In this shape space a new metric was recently introduced with the result that this shape space has the property of being isometric to an infinite-dimensional Grassmann manifold of 2-dimensional subspaces. Using this isometry it is possible, from Younes et al. (2008), to explicitly describe geodesics, a task that previously was not at all easy. Our aim is twofold, namely: to use this general theory in order to show some applications to the study of erythrocytes, using digital images of peripheral blood smears, in the treatment of sickle cell disease; and, since normal erythrocytes are almost circular and many Sickle cells have elliptical shape, to particularize the computation of geodesics and distances between shapes using this metric to planar objects considered as deformations of a template (circle or ellipse). The applications considered include: shape interpolation, shape classification, and shape clustering.
- INTELLIGENT DETECTION AND CLASSIFICATION OF MICROCALCIFICATION IN COMPRESSED MAMMOGRAM IMAGE
Benjamin Joseph, Baskaran Ramachandran, Priyadharshini Muthukrishnan
The main contribution of this article is introducing an intelligent classifier to distinguish between benign and malignant areas of micro-calcification in companded mammogram image which is not proved or addressed elsewhere. This method does not require any manual processing technique for classification, thus it can be assimilated for identifying benign and malignant areas in intelligent way. Moreover it gives good classification responses for compressed mammogram image. The goal of the proposed method is twofold: one is to preserve the details in Region of Interest (ROI) at low bit rate without affecting the diagnostic related information and second is to classify and segment the micro-calcification area in reconstructed mammogram image with high accuracy. The prime contribution of this work is that details of ROI and Non-ROI regions extracted using multi-wavelet transform are coded at variable bit rate using proposed Region Based Set Partitioning in Hierarchical Trees (RBSPIHT) before storing or transmitting the image. Image reconstructed during retrieval or at the receiving end is preprocessed to remove the channel noise and to enhance the diagnostic contrast information. Then the preprocessed image is classified as normal or abnormal (benign or malignant) using Probabilistic neural network. Segmentation of cancerous region is done using Fuzzy C-means Clustering (FCC) algorithm and the cancerous area is computed. The experimental result shows that the proposed model performance is good at achieving high sensitivity of 97.27%, specificity of 94.38% at an average compression rate and Peak Signal to Noise Ratio (PSNR) of 0.5bpp and 58dB respectively.
- VARIABILITY OF MANUAL AND COMPUTERIZED METHODS FOR MEASURING CORONAL VERTEBRAL INCLINATION IN COMPUTED TOMOGRAPHY IMAGES
Tomaž Vrtovec, Franjo Pernuš, Boštjan Likar
Objective measurement of coronal vertebral inclination (CVI) is of significant importance for evaluating spinal deformities in the coronal plane. The purpose of this study is to systematically analyze and compare manual and computerized measurements of CVI in cross-sectional and volumetric computed tomography (CT) images. Three observers independently measured CVI in 14 CT images of normal and 14 CT images of scoliotic vertebrae by using six manual and two computerized measurements. Manual measurements were obtained in coronal cross-sections by manually identifying the vertebral body corners, which served to measure CVI according to the superior and inferior tangents, left and right tangents, and mid-endplate and mid-wall lines. Computerized measurements were obtained in two dimensions (2D) and in three dimensions (3D) by manually initializing an automated method in vertebral centroids and then searching for the planes of maximal symmetry of vertebral anatomical structures. The mid-endplate lines were the most reproducible and reliable manual measurements (intra- and inter-observer variability of 0.7° and 1.2° standard deviation, SD, respectively). The computerized measurements in 3D were more reproducible and reliable (intra- and inter-observer variability of 0.5° and 0.7° SD, respectively), but were most consistent with the mid-wall lines (2.0° SD and 1.4° mean absolute difference). The manual CVI measurements based on mid-endplate lines and the computerized CVI measurements in 3D resulted in the lowest intra-observer and inter-observer variability, however, computerized CVI measurements reduce observer interaction.
- A PSO MODEL FOR DISEASE PATTERN DETECTION ON LEAF SURFACES
Kanthan Muthukannan, Pitchai Latha
The main objective of this paper is to segment the disease affected portion of a plant leaf and extract the hybrid features for better classification of different disease patterns. A new approach named as Particle Swarm Optimization (PSO) is proposed for image segmentation. PSO is an automatic unsupervised efficient algorithm which is used for better segmentation and better feature extraction. Features extracted after segmentation are important for disease classification so that the hybrid feature extraction components controls the accuracy of classification for different diseases. The approach named as Hybrid Feature Extraction (HFE), which has three components namely color, texture and shape based features. The performance of the preprocessing result was compared and the best result was taken for image segmentation using PSO. Then the hybrid feature parameters were extracted from the gray level co-occurrence matrices of different leaves. The proposed method was tested on different images of disease affected leaves, and the experimental results exhibit its effectiveness.
- CORRIGENDUM: A MIXED BOOLEAN AND DEPOSIT MODEL FOR THE MODELING OF METAL PIGMENTS IN PAINT LAYERS (2015 IMAGE ANAL STEREOL 34:125–34)
Enguerrand Couka, François Willot, Dominique Jeulin
Corrigendum of A MIXED BOOLEAN AND DEPOSIT MODEL FOR THE MODELING OF METAL PIGMENTS IN PAINT LAYERS (2015 IMAGE ANAL STEREOL 34:125–34).