Preprocessing and Gridding of Microarray Image Using Mathematical Morphology in Signal Processing

2013 ◽  
Vol 8 (11) ◽  
pp. 502-510
Author(s):  
Rathiah Hashim ◽  
Nurnabilah Samsudin ◽  
Noor Elaiza Abdul Khalid
Author(s):  
Jose Crespo

In the last fifty years, approximately, advances in computers and the availability of images in digital form have made it possible to process and to analyze them in automatic (or semi-automatic) ways. Alongside with general signal processing, the discipline of image processing has acquired a great importance for practical applications as well as for theoretical investigations. Some general image processing references are (Castleman, 1979) (Rosenfeld & Kak, 1982) (Jain, 1989) (Pratt, 1991) (Haralick & Shapiro, 1992) (Russ, 2002) (Gonzalez & Woods, 2006). Mathematical Morphology, which was founded by Serra and Matheron in the 1960s, has distinguished itself from other types of image processing in the sense that, among other aspects, has focused on the importance of shapes. The principles of Mathematical Morphology can be found in numerous references such as (Serra, 1982) (Serra, 1988) (Giardina & Dougherty, 1988) (Schmitt & Mattioli, 1993) (Maragos & Schafer, 1990) (Heijmans, 1994) (Soille, 2003) (Dougherty & Lotufo, 2003) (Ronse, 2005).


2013 ◽  
Vol 462-463 ◽  
pp. 280-283
Author(s):  
Hui Wang ◽  
Qi Li

The traditional vibration signal de-noising method has the disadvantage of inefficiency and incomplete information acquisition.The signal processing method based on mathematical morphology has the advantage of simple calculation, good in real time, little time delay, and can help filter while maximizing retention the basic characteristics of the signal analysis process. This article is based on mathematical morphology dilation, erosion, opening and closing the four basic operations, carried out the vibration signal de-noising processing, simulation and experimental results show that the method is effective and practical.


2017 ◽  
Author(s):  
Jian Chen ◽  
Hong Chen ◽  
Xiaoxia Cai ◽  
Pengfei Weng ◽  
Hao Nie

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