By El-Sayed M. El-Alfy, Sabu M. Thampi, Hideyuki Takagi, Selwyn Piramuthu, Thomas Hanne
This publication incorporates a number of refereed and revised papers of clever Informatics tune initially awarded on the 3rd overseas Symposium on clever Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers chosen for this tune conceal numerous clever informatics and similar subject matters together with sign processing, trend reputation, photograph processing facts mining and their applications.
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Top and left) is computed and the reference pixel is replaced with this value. In this project the threshold value is set to ten. Thus the process of quantization and aggregation reduces the time for the convergence of the FCM algorithm . The concept of aggregation is explained by considering a sample dataset as in Table 2. It can be observed from Table 2 that by masking three lower bits certain intensity values become same in their binary value. The intensity values 11, 13, and 10 after quantization becomes 8 and the intensity values 16 and 18 after quantization becomes 16.
Shape based features: Eccentricity, major axis length and minor axis length. GLRL features: Short run emphasis, long run emphasis, gray level non uniformity, run percentage, run length non uniformity, low gray level run emphasis and high gray level run emphasis. GLCM features: Contrast, homogeneity, energy, correlation, inverse difference normalized, inverse difference is homomorphic, info measure of correlation-I, info measure of correlation-II, Difference entropy, Difference variance, sum entropy, sum variance, sum of squares, maximum probability, sum average, dissimilarity, cluster shade and cluster prominence.
Have proposed the use of modified cluster center and membership updating function in the conventional FCM algorithm to segment the tumor region from brain MRI images . Shasidhar et al. have proposed a method for modifying the conventional FCM algorithm by using quantization and aggregation as a result of which the dataset is reduced . Fahran et al. Wang et al. have proposed the use of region based active contour model along with Local Gaussian Distribution (LGD) fitting energy for the contour to evolve .