6. Experiments and results

Experimental work has been carried out on the system with Intel(R) Core(TM) i3-5005U CPU@2.00GHz processor speed, 4GB RAM, Windows 7 OS (64-bit) and using JDK1.7.0\_45. Separate modules are written for each of the above discussed methods to observe the CPU time for clustering any dataset by keeping the cluster seeds same for all methods. I/O operations are eliminated and the CPU time observed is strictly for clustering of the data.

Along with the newly developed hybrid algorithms, experiments are also conducted with the algorithms for standard K-Means (KM) and Fuzzy C-Means (FCM) for performance comparison. All the algorithms are executed using datasets that are related to four images. The details of these images are available in Table 1.


#### Table 1. Medical Images.

The medical images used in the present experiment are heart image [44] and kidneys image [45] (Figures 1 and 2). The experiments are also carried out using two benchmark images. They are Baboon and Lena images [46] (Figures 3 and 4).

Below is the brief description of medical images.

The Heart is a medical image obtained from biology data repository [44]. It is in "jpeg" format. The 'Kidneys' is a colored MRI scan of a coronal section through a human abdomen, showing the front view of healthy kidneys and liver [45]. It is in 'jpeg' format. The Baboon and Lena are benchmark test images that are found frequently in the literature [46]. These are all in uncompressed "tif" format.

All the algorithms for standard K-Means (KM), standard Fuzzy C-Means (FCM), KMFCM and KMandFCM are executed on each image data with varying number of clusters (k = 10, 11, 12, 13, 14, 15). For all algorithms, same cluster seeds are used. Same termination condition Eq. (2) is used for all the experiments. The details of CPU time, clustering fitness and SSE of each algorithm for the all images are given in the following sub-sections (Tables 2–13). The results are also projected in their respective graphs (Figures 5–16).

Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms DOI: http://dx.doi.org/10.5772/intechopen.86374
