Is There a Fingerprint Pattern in the Image?

A fingerprint orientation field has distinct characteristics which can differentiate fingerprints from any other flow patterns: it has a specific number of singular points (cores and deltas), the configuration of singular points follows a certain spatial distribution, and its global shape is like an arch. In this paper, we propose a global fingerprint orientation field model, represented in terms of ordinary differential equations, which does not require any prior information such as singular points or orientation of a fingerprint. Further, the model requires only a small number of polynomial terms to represent the global fingerprint orientation field. The coefficients of the model are found subject to the constraints on the total number of singular points (i.e., 0, 2, or 4) in a fingerprint. The proposed model is used to distinguish fingerprint images from non-fingerprint images and altered fingerprints by measuring the abnormality in the orientation field of the image. 1

Modeling, Validation And Verification Of Cell-Scaffold Contact Measurements Over Terabyte-Sized 3D Image Collection

This poster presents the problem of 3D contact measurements from two co-registered volumetric images (z-stacks). The 3D contact measurement consists of (a) segmenting an object of interest in each z-stack, (b) computing the relative spatial positions of the detected objects to detect contacts, (c) validating the accuracy of segmentation, and (d) visually verifying correct contact detection. The 3D measurement has to overcome challenges related to (1) intensity bleed-through across co-registered volumes, (2) insufficient knowledge about statistics and geometry of objects, (3) large RAM requirements (∼3GB just to load the input data) and data volume (\u3e1TB), and (4) complexity of 3D visual inspection

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). Our analysis of the acquired terabyte-sized collection is motivated by the need to understand the nature of the shape dimensionality (1D vs 2D vs 3D) of cell-scaffold interactions relevant to tissue engineers that grow cells on biomaterial scaffolds. Results: We designed five statistical and three geometrical contact models, and then down-selected them to one from each category using a validation approach based on physically orthogonal measurements to CLSM. The two selected models were applied to 414 z-stacks with three scaffold types and all contact results were visually verified. A planar geometrical model for the spun coat scaffold type was validated from atomic force microscopy images by computing surface roughness of 52.35 nm ±31.76 nm which was 2 to 8 times smaller than the CLSM resolution. A cylindrical model for fiber scaffolds was validated from multi-view 2D scanning electron microscopy (SEM) images. The fiber scaffold segmentation error was assessed by comparing fiber diameters from SEM and CLSM to be between 0.46% to 3.8% of the SEM reference values. For contact verification, we constructed a web-based visual verification system with 414 pairs of images with cells and their segmentation results, and with 4968 movies with animated cell, scaffold, and contact overlays. Based on visual verification by three experts, we report the accuracy of cell segmentation to be 96.4% with 94.3% precision, and the accuracy of cell-scaffold contact for a statistical model to be 62.6% with 76.7% precision and for a geometrical model to be 93.5% with 87.6% precision. Conclusions: The novelty of our approach lies in (1) representing cell-scaffold contact sites with statistical intensity and geometrical shape models, (2) designing a methodology for validating 3D geometrical contact models and (3) devising a mechanism for visual verification of hundreds of 3D measurements. The raw and processed data are publicly available from https://isg.nist.gov/deepzoomweb/data/together with the web -based verification system

Additional file 6: of Modeling, validation and verification of three-dimensional cell-scaffold contacts from terabyte-sized images

Algorithms based on statistical models for segmenting all scaffold types. (DOCX 48 kb