Linear Feature Detection on GPUs

The acceleration of an existing linear feature detection algorithm for 2D images using GPUs is discussed. The two most time consuming components of this process are implemented on the GPU, namely, linear feature detection using dual-peak directional non-maximum suppression, and a gap filling process that joins disconnected feature masks to rectify false negatives. Multiple steps or image filters in each component are combined into a single GPU kernel to minimise data transfers to off-chip GPU RAM, and issues relating to on-chip memory utilisation, caching, and memory coalescing are considered. The presented algorithm is useful for applications needing to analyse complex linear structures, and examples are given for dense neurite images from the biotech domain.

[1]  Mathews Jacob,et al.  Design of steerable filters for feature detection using canny-like criteria , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Hugues Talbot,et al.  Directional Morphological Filtering , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Steve Mann,et al.  Using graphics devices in reverse: GPU-based Image Processing and Computer Vision , 2008, 2008 IEEE International Conference on Multimedia and Expo.

[4]  Michael Shantz,et al.  Multi-level texture caching for 3D graphics hardware , 1998, Proceedings. 25th Annual International Symposium on Computer Architecture (Cat. No.98CB36235).

[5]  Hong Shen,et al.  Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms , 1999, IEEE Transactions on Information Technology in Biomedicine.

[6]  Anoop Gupta,et al.  The Design and Analysis of a Cache Architecture for Texture Mapping , 1997, ISCA.

[7]  Yannis A. Tolias,et al.  A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering , 1998, IEEE Transactions on Medical Imaging.

[8]  G. Amdhal,et al.  Validity of the single processor approach to achieving large scale computing capabilities , 1967, AFIPS '67 (Spring).

[9]  K. Ramesh Babu,et al.  Linear Feature Extraction and Description , 1979, IJCAI.

[10]  Changming Sun,et al.  Circular shortest path in images , 2003, Pattern Recognit..

[11]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[12]  Tim Ritchings,et al.  Image segmentation using maximum gradient profiles orthogonal to edges , 1990, Image Vis. Comput..

[13]  Martin A. Fischler,et al.  Detection of roads and linear structures in low-resolution aerial imagery using a multisource knowledge integration technique☆ , 1981 .

[14]  P. Ramm,et al.  Automated Screening of Neurite Outgrowth , 2003, Journal of biomolecular screening.

[15]  J. Canny.  Finding Edges and Lines in Images , 1983 .

[16]  Rony Nuydens,et al.  Automatic quantification of neurite outgrowth by means of image analysis , 2004, SPIE BiOS.

[17]  David H. Eberly,et al.  Ridges in Image and Data Analysis , 1996, Computational Imaging and Vision.

[18]  Mathews Jacob,et al.  A Novel Approach to Neurite Tracing in Fluorescence Microscopy Images , 2003, SIP.

[19]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[20]  Changming Sun,et al.  Automated analysis of neurite branching in cultured cortical neurons using HCA‐Vision , 2007, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[21]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[22]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[23]  Changming Sun,et al.  Fast Linear Feature Detection Using Multiple Directional Non-Maximum Suppression , 2006, ICPR.

[24]  Jens H. Krüger,et al.  A Survey of General‐Purpose Computation on Graphics Hardware , 2007, Eurographics.