Spot detection for a 2-DE gel image using a slice tree with confidence evaluation

Authors:
Yi-Sheng Liu;Shu-Yuan Chen;Ru-Sheng Liu;Der-Jyh Duh;Ya-Ting Chao;Yuan-Ching Tsai;Jaw-Shu Hsieh
Affiliations:
Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan;Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan;Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan;Department of Computer Science and Information Engineering, Ching Yun University, Chung Li, Taiwan;Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan;Department of Agronomy, National Taiwan University, Taipei, Taiwan;Department of Agronomy, National Taiwan University, Taipei, Taiwan
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2009

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Abstract

Spot detection is an essential step in 2-DE gel image analysis. The results of protein spot detection may substantially influence subsequent stages of analysis. This study presents a novel method for spot detection with the addition of confidence evaluation for each detected spot. The confidence of a spot provides useful hints for subsequent processing, such as landmark selection, spot quantification and gel image registration. The proposed method takes slices of a gel image in the gray level direction, and builds them into a slice tree, which in turn is adopted to perform spot detection and confidence evaluation. The spot detection software is implemented on Windows using the proposed slice tree. Building a slice tree for a gel image of resolution 1262x720 takes about 1.5 s on an Intel^(C)Pentium^(C)III 1.2 GHz machine with 512 MB of RAM. Spot detection takes about 43 ms after building the slice tree. The detected spots are shown by different colors based on their respective confidence values. Moreover, pointing a mouse over a detected spot shows detailed information about the spot, including the confidence value. Experimental results indicate that confidence values are close to a subjective judgment.