Segmentation of Salivary Glands in Nuclear Medicine Images Using Edge Detection Tools
AbstractRecognition of the Salivary glands in nuclear medicine examination is very difficult because of unclear borders and existence of noise, which affects the spatial resolution and reduces the diagnostic values of those images Therefore, image-processing programs such as MatLab, has powerful tools, which can use for solving those problems. The Morphology tool frequently applied to this problem. In this paper, I used entropyfilt function to create a texture image. This function returns an array where each output pixel contains the entropy value of the 9-by-9 neighborhood around the corresponding pixel in the salivary glands scintigraphy images. Threshold the rescaled image to segment the textures. A threshold value of 0.8 selected because it was roughly the intensity value of pixels along the boundary between the textures. The segmented images compare the binary image rough Mask to the original image. The quantitative results calculated using a measure of percentage match between ground truth and segmentation results. The percentage match (PM) measure was 99.33 (p ˂0.05) and Corresponding Ratio (CR) was -0.007 p ˂0.05). The proposed method is able to recognize the salivary glands accurately.
(1) Shahid S. Higher Order Statistics Techniques Applied to EMG Signal Analysis and Characterization. Ph.D. thesis, University of Limerick; Ireland, 2004.
(2) Nikias CL, Raghuveer MR. Bispectrum estimation: A digital signal processing framework. IEEE Proceedings on Communications and Radar. 1987; 75 (7):869–891.
(3) Basmajian JV, de Luca CJ. Muscles Alive - The Functions Revealed by Electromyography. The Williams & Wilkins Company; Baltimore, 1985.
(4) Cram JR, Kasman GS, Holtz J. Introduction to Surface Electromyography. Aspen Publishers Inc.; Gaithersburg, Maryland, 1998.
(5) Thexton AJ. A randomization method for discriminating between signal and noise in recordings of rhythmic electromyographic activity. J Neurosci Meth. 1996; 66: 93 –98.
(6) Bornato P, de Alessio T, Knaflitz M. A statistical method for the measurement of the muscle activation intervals from surface myoelectric signal gait. IEEE Trans Biomed Eng.1998; 45: 287–299. doi:
(7) Merlo A, Farina D. A Fast and Reliable Technique for Muscle Activity Detection from Surface EMG Signals. IEEE Trans Biomed Eng. 2003; 50 (3):316–323. Doi: 10.1109/TBME.2003.808829.
(8) Gabor D. Theory of communication. J Inst Elect Eng.1946; 93:429–457.
(9) Hefftner G, Zucchini W, Jaros G. The electromyogram (EMG) as a control signal for functional neuro-muscular stimulation part 1: Autoregressive modeling as a means of EMG signature discrimination. IEEE Trans Biomed Eng.1988; 35:230–237. doi: 10.1109/10.1370.
(10) Christodoulou CI, Pattichis CS. A new technique for the classification and decomposition of EMG signals. Proceedings in IEEE International Conference on Neural Networks.1995; 5:2303–2308.
(11) H. Arieta, R. Katoh, H. Yokoi, Y. Wenwei, 2006Development of a multi-DOF electromyography prosthetic system using the adaptive joint mechanism, ABBI 2006, 32110.