Fuzzy C-means Segmentation Method Research Articles

Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Plants have assumed a significant role in the history of humankind, for the most part as a source of nourishment for human and animals. However, plants typically powerless to different sort of diseases such as leaf blight, gray spot and rust. It will cause a great loss to farmers and ranchers. Therefore, an appropriate method to estimate the severity of diseases in plant leaf is needed to overcome the problem. This paper presents the fusions of the Fuzzy C-Means segmentation method with four different colour spaces namely RGB, HSV, L*a*b and YCbCr to estimate plant leaf disease severity. The percentage of performance of proposed algorithms are recorded and compared with the previous method which are K-Means and Otsu’s thresholding. The best severity estimation algorithm and colour space used to estimate the diseases severity of plant leaf is the combination of Fuzzy C-Means and YCbCr color space. The average performance of Fuzzy C-Means is 91.08% while the average performance of YCbCr is 83.74%. Combination of Fuzzy C-Means and YCbCr produce 96.81% accuracy. This algorithm is more effective than other algorithms in terms of not only better segmentation performance but also low time complexity that is 34.75s in average with 0.2697s standard deviation.

A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection

Background and objectiveFor cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in the image the segmentation results may not be accurate. The objective is to propose an efficient fuzzy c-means based segmentation approach which can also handle the noise present in the image during the segmentation process itself i.e. noise removal and segmentation is combined in one step. MethodsTo address the above issues, in this paper a fourth order partial differential equation (FPDE) based nonlinear filter adapted to Poisson noise with fuzzy c-means segmentation method is proposed. This approach is capable of effectively handling the segmentation problem of blocky artifacts while achieving good tradeoff between Poisson noise removals and edge preservation of the microscopic biopsy images during segmentation process for cancer detection from cells. ResultsThe proposed approach is tested on breast cancer microscopic biopsy data set with region of interest (ROI) segmented ground truth images. The microscopic biopsy data set contains 31 benign and 27 malignant images of size 896 × 768. The region of interest selected ground truth of all 58 images are also available for this data set. Finally, the result obtained from proposed approach is compared with the results of popular segmentation algorithms; fuzzy c-means, color k-means, texture based segmentation, and total variation fuzzy c-means approaches. ConclusionsThe experimental results shows that proposed approach is providing better results in terms of various performance measures such as Jaccard coefficient, dice index, Tanimoto coefficient, area under curve, accuracy, true positive rate, true negative rate, false positive rate, false negative rate, random index, global consistency error, and variance of information as compared to other segmentation approaches used for cancer detection.

Effective FCM noise clustering algorithms in medical images

The main motivation of this paper is to introduce a class of robust non-Euclidean distance measures for the original data space to derive new objective function and thus clustering the non-Euclidean structures in data to enhance the robustness of the original clustering algorithms to reduce noise and outliers. The new objective functions of proposed algorithms are realized by incorporating the noise clustering concept into the entropy based fuzzy C-means algorithm with suitable noise distance which is employed to take the information about noisy data in the clustering process. This paper presents initial cluster prototypes using prototype initialization method, so that this work tries to obtain the final result with less number of iterations. To evaluate the performance of the proposed methods in reducing the noise level, experimental work has been carried out with a synthetic image which is corrupted by Gaussian noise. The superiority of the proposed methods has been examined through the experimental study on medical images. The experimental results show that the proposed algorithms perform significantly better than the standard existing algorithms. The accurate classification percentage of the proposed fuzzy C-means segmentation method is obtained using silhouette validity index.

Robust kernel FCM in segmentation of breast medical images

This paper presents an automatic effective fuzzy c-means segmentation method for segmenting breast cancer MRI based on standard fuzzy c-means. To introduce a new effective segmentation method, this paper introduced a novel objective function by replacing original Euclidean distance on feature space using new hyper tangent function. This paper obtains the new hyper tangent function from exited hyper tangent function to perform effectively with large number of data from more noised medical images and to have strong clusters. It derives an effective method to construct the membership matrix for objects, and it derives a robust method for updating centers from proposed novel objective function. Experiments will be done with an artificially generated data set to show how effectively the new fuzzy c-means obtain clusters, and then this work implements the proposed methods to segment the breast medical images into different regions, each corresponding to a different tissue, based on the signal enhancement-time information. This paper compares the results with results of standard fuzzy c-means algorithm. The correct classification rate of proposed fuzzy c-means segmentation method is obtained using silhouette method.

Image‐processing chain for a three‐dimensional reconstruction of basal cell carcinomas*†

Basal cell carcinoma (BCC) is the most common malignant skin cancer. For a deeper insight into the specific growth patterns of the tumorous tissue in BCC, we have focused on the development of a novel automated image-processing chain for 3D reconstruction of BCC using histopathological serial sections. For fully automatic delineation of the tumor within the tissue, we apply a fuzzy c-means segmentation method. We used a novel multi-grid form of the non-linear registration introduced by Braumann and Kuska in 2005 effectively suppressing registration runs into local minima (possibly caused by diffuse nature of the tumor). Our method was successfully applied in a proof-of-principle study for automated reconstruction.