Mahalanobis Fuzzy C-Means Clustering with Spatial Information for Image Segmentation

Algoritma segmentasi Fuzzy C-Means dapat diimplementasikan pada segmentasi citra berdasarkan jarak mahalanobis; Namun, metode ini hanya perlu mempertimbangkan situasi ruang warna, bukan sistem ketetanggaan citra. itu adalah efek proses deteksi tepi yang tidak berjalan dengan baik dan menghasilkan akurasi yang kurang dalam hasil segmentasi. Pada artikel ini, kami mengusulkan metode baru untuk segmentasi citra dengan Mahalanobis fuzzy C-means Spatial information (MFCMS). Metode yang diusulkan menggabungkan ruang fitur dan citra informasi lingkungan (informasi spasial) untuk meningkatkan akurasi hasil segmentasi pada citra. MFCMS terdiri dari dua Langkah, modul histogram threshold untuk langkah pertama dan modul MFCMS untuk langkah kedua. Modul Threshold Histogram digunakan untuk mendapatkan kondisi inisialisasi MFCMS untuk centroid cluster dan jumlah centroid. Hasil pengujian menunjukkan bahwa metode ini memberikan kinerja segmentasi yang lebih baik daripada kesalahan klasifikasi (ME) dan kesalahan area latar depan relatif (RAE) masing-masing sebesar 1,61 dan 3,48

Similarity based hierarchical clustering of physiological parameters for the identification of health states - a feasibility study

This paper introduces a new unsupervised method for the clustering of physiological data into health states based on their similarity. We propose an iterative hierarchical clustering approach that combines health states according to a similarity constraint to new arbitrary health states. We applied method to experimental data in which the physical strain of subjects was systematically varied. We derived health states based on parameters extracted from ECG data. The occurrence of health states shows a high temporal correlation to the experimental phases of the physical exercise. We compared our method to other clustering algorithms and found a significantly higher accuracy with respect to the identification of health states.Comment: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC

Volume and shape in feature space on adaptive FCM in MRI segmentation.

Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster\u27s shape in the feature space are incorporated into the fuzzy c-means (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification

Anisotropic mean shift based fuzzy c-means segmentation of deroscopy images

Image segmentation is an important task in analysing dermoscopy images as the extraction of the borders of skin lesions provides important cues for accurate diagnosis. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. Fuzzy c-means has been shown to work well for clustering based segmentation, however due to its iterative nature this approach has excessive computational requirements. In this paper, we introduce a new mean shift based fuzzy c-means algorithm that requires less computational time than previous techniques while providing good segmentation results. The proposed segmentation method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centers, the entire strategy is capable of effectively detecting regions within an image. Experimental results on a large dataset of diverse dermoscopy images demonstrate that the presented method accurately and efficiently detects the borders of skin lesions

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster\u27s shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures