Graylevel alignment between two images using linear programming

A critical step in defect detection for semiconductor process is to align a test image against a reference. This includes both spatial alignment and grayscale alignment. For the latter, a direct least square approach is not very applicable because the presence of defects would skew the parameters. Instead, we use a linear programming formulation which has the advantage of having a fast algorithm, while at the same time can produce better alignment of the test image to the reference. Furthermore, this is a flexible algorithm capable of incorporating additional constraints, such as ensuring that the aligned pixel values are within the allowable intensity range.published_or_final_versio

3D particle tracking velocimetry using dynamic discrete tomography

Particle tracking velocimetry in 3D is becoming an increasingly important imaging tool in the study of fluid dynamics, combustion as well as plasmas. We introduce a dynamic discrete tomography algorithm for reconstructing particle trajectories from projections. The algorithm is efficient for data from two projection directions and exact in the sense that it finds a solution consistent with the experimental data. Non-uniqueness of solutions can be detected and solutions can be tracked individually

Detection thresholding using mutual information

In this paper, we introduce a novel non-parametric thresholding method that we term Mutual-Information Thresholding. In our approach, we choose the two detection thresholds for two input signals such that the mutual information between the thresholded signals is maximised. Two efficient algorithms implementing our idea are presented: one using dynamic programming to fully explore the quantised search space and the other method using the Simplex algorithm to perform gradient ascent to significantly speed up the search, under the assumption of surface convexity. We demonstrate the effectiveness of our approach in foreground detection (using multi-modal data) and as a component in a person detection system

Automatic knee joint space measurement from plain radiographs

Abstract. Knee osteoarthritis is a common joint disease and one of the leading causes of disability. The disease is characterized by loss of articular cartilage and bone remodeling. Tissue deformations eventually lead to joint space narrowing which can be detected from plain radiographs. Joint space narrowing is typically measured by an experienced radiologist manually, which can be time consuming and error prone process. The aim of this study was to develop and evaluate a fully automatic joint space width measurement method for bilateral knee radiographs. The knee joint was localized from the x-ray images using template matching and the joint space was delineated using active shape model (ASM). Two different automatic joint space measurement methods were tested and the results were validated against manual measurements performed by an experienced researcher. The first joint space width measurements were done by binarizing the joint space and measuring the local thickness of the binary mask using disk fitting. The second method classified bone pixels to tibia and femur. Classification was based on the ASM delineation. Nearest neighbors between femur and tibia were then used to find the joint space width. An automatic method for tibial region of interest (ROI) selection was also implemented. The algorithms used in this thesis were also made publicly available online. The automatically obtained joint space widths were in line with manual measurements. Higher accuracy was obtained using the disk fitting algorithm. Automatic Tibial ROI selection was accurate, although the orientation of the joint was ignored in this study. An open source software with a simple graphical user interface and visualization tools was also developed. Computationally efficient and easily explainable methods were utilized in order to improve accessibility and transparency of computer assisted diagnosis of knee osteoarthritis.Tiivistelmä. Polvinivelrikko on eräs yleisimpiä niveltauteja sekä yksi merkittävimmistä liikuntavammojen aiheuttajista. Nivelrikolle ominaisia piirteitä ovat nivelruston vaurioituminen ja muutokset nivelrustonalaisessa luussa. Kudosten muutokset ja vauriot johtavat lopulta niveltilan kaventumiseen, mikä voidaan havaita röntgenkuvista. Tavallisesti kokenut radiologi tekee niveltilan mittaukset manuaalisesti, mikä vaatii usein paljon aikaa ja on lisäksi virhealtis prosessi. Tämän tutkielman tavoitteena oli kehittää täysin automaattinen niveltilan mittausmenetelmä bilateraalisille polven röntgenkuville. Polvinivel paikallistettiin röntgenkuvista muotoon perustuvalla hahmontunnistuksella ja nivelväli rajattiin käyttämällä aktiivista muodon sovitusta (active shape model, ASM). Nivelvälin mittaukseen käytettiin kahta eri menetelmää, joita verrattiin kokeneen tutkijan tekemiin manuaalisiin mittauksiin. Ensimmäinen nivelvälin mittausmenetelmä sovitti ympyränmuotoisia maskeja niveltilasta tehtyyn binäärimaskiin. Toinen mittausmenetelmä luokitteli luuhun kuuluvat pikselit sääri- ja reisiluuhun. Luokittelu perustui aikaisemmin tehtyyn automaattiseen nivelvälin rajaukseen. Nivelvälin mittaukseen käytettiin lähimpiä naapuripikseleitä sääri- ja reisiluusta. Työssä kehitettiin myös menetelmä automaattiseen sääriluun mielenkiintoalueiden (region of interest, ROI) valintaan. Käytetyt algoritmit ovat julkisesti saatavilla verkossa. Automaattiset nivelväli mittaukset vastasivat manuaalisia mittauksia hyvin. Parempi tarkkuus saatiin käyttämällä ympyrän sovitusta hyödyntävää algoritmia nivelvälin mittaukseen. Sääriluun mielenkiintoalueet onnistuttiin määrittämään automaattisesti, tosin nivelen orientaatiota ei huomioitu tässä työssä. Lisäksi kehitettiin avoimen lähdekoodin ohjelmisto yksinkertaisella graafisella käyttöliittymällä ja visualisointityökaluilla. Työssä käytettiin laskennallisesti tehokkaita ja helposti selitettäviä menetelmiä, mikä edesauttaa tietokoneavusteisen menetelmien käyttöä polvinivelrikon tutkimuksessa

Real-Time Automatic Linear Feature Detection in Images

Linear feature detection in digital images is an important low-level operation in computer vision that has many applications. In remote sensing tasks, it can be used to extract roads, railroads, and rivers from satellite or low-resolution aerial images, which can be used for the capture or update of data for geographic information and navigation systems. In addition, it is useful in medical imaging for the extraction of blood vessels from an X-ray angiography or the bones in the skull from a CT or MR image. It also can be applied in horticulture for underground plant root detection in minirhizotron images. In this dissertation, a fast and automatic algorithm for linear feature extraction from images is presented. Under the assumption that linear feature is a sequence of contiguous pixels where the image intensity is locally maximal in the direction of the gradient, linear features are extracted as non-overlapping connected line segments consisting of these contiguous pixels. To perform this task, point process is used to model line segments network in images. Specific properties of line segments in an image are described by an intensity energy model. Aligned segments are favored while superposition is penalized. These constraints are enforced by an interaction energy model. Linear features are extracted from the line segments network by minimizing a modified Candy model energy function using a greedy algorithm whose parameters are determined in a data-driven manner. Experimental results from a collection of different types of linear features (underground plant roots, blood vessels and urban roads) in images demonstrate the effectiveness of the approach

Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry

Quick mass production of homogeneous thin film material is required in paper, plastic, fabric, and thin film industries. Due to the high feed rates and small thicknesses, machine vision and other nondestructive evaluation techniques are used to ensure consistent, defect-free material by continuously assessing post-production quality. One of the fastest growing inspection areas is for 0.5-500 micrometer thick thin films, which are used for semiconductor wafers, amorphous photovoltaics, optical films, plastics, and organic and inorganic membranes. As a demonstration application, a prototype roll-feed imaging system has been designed to inspect high-temperature polymer electrolyte membrane (PEM), used for fuel cells, after being die cast onto a moving transparent substrate. The inspection system continuously detects thin film defects and classifies them with a neural network into categories of holes, bubbles, thinning, and gels, with a 1.2% false alarm rate, 7.1% escape rate, and classification accuracy of 96.1%. In slot die casting processes, defect types are indicative of a misbalance in the mass flow rate and web speed; so, based on the classified defects, the inspection system informs the operator of corrective adjustments to these manufacturing parameters. Thickness uniformity is also critical to membrane functionality, so a real-time, full-field transmission densitometer has been created to measure the bi-directional thickness profile of the semi-transparent PEM between 25-400 micrometers. The local thickness of the 75 mm x 100 mm imaged area is determined by converting the optical density of the sample to thickness with the Beer-Lambert law. The PEM extinction coefficient is determined to be 1.4 D/mm and the average thickness error is found to be 4.7%. Finally, the defect inspection and thickness profilometry systems are compiled into a specially-designed graphical user interface for intuitive real-time operation and visualization.M.S.Committee Chair: Tequila Harris; Committee Member: Levent Degertekin; Committee Member: Wayne Dale

Image Interpolation on the CPU and GPU using Line Run Sequences

This paper describes an efficient implementation of an image interpolation algorithm based on inverse distance weighting (IDW). The time-consuming search for support pixels bordering the voids to be filled is facilitated through gapless sweeps of different directions over the image. The scanlines needed for the sweeps are constructed from a path prototype per orientation whose regular substructures get reused and shifted to produce aligned duplicates covering the entire input bitmap. The line set is followed concurrently to detect existing samples around nodata patches and compute the distance to the pixels to be newly set. Since the algorithm relies on integer line rasterization only and does not need auxiliary data structures beyond the output image and weight aggregation bitmap for intensity normalization, it will run on multi-core central and graphics processing units (CPUs and GPUs). Also, occluded support pixels of non-convex void patches are ignored, and over- or undersampling close-by and distant valid neighbors is compensated. Runtime and accuracy compared to generated IDW ground truth get evaluated for the CPU and GPU implementation of the algorithm on single-channel and multispectral bitmaps of various filling degrees

Making microscopy count: quantitative light microscopy of dynamic processes in living plants

First published: April 2016This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Cell theory has officially reached 350 years of age as the first use of the word ‘cell’ in a biological context can be traced to a description of plant material by Robert Hooke in his historic publication “Micrographia: or some physiological definitions of minute bodies”. The 2015 Royal Microscopical Society Botanical Microscopy meeting was a celebration of the streams of investigation initiated by Hooke to understand at the sub-cellular scale how plant cell function and form arises. Much of the work presented, and Honorary Fellowships awarded, reflected the advanced application of bioimaging informatics to extract quantitative data from micrographs that reveal dynamic molecular processes driving cell growth and physiology. The field has progressed from collecting many pixels in multiple modes to associating these measurements with objects or features that are meaningful biologically. The additional complexity involves object identification that draws on a different type of expertise from computer science and statistics that is often impenetrable to biologists. There are many useful tools and approaches being developed, but we now need more inter-disciplinary exchange to use them effectively. In this review we show how this quiet revolution has provided tools available to any personal computer user. We also discuss the oft-neglected issue of quantifying algorithm robustness and the exciting possibilities offered through the integration of physiological information generated by biosensors with object detection and tracking