A novel fuzzy clustering algorithm for the analysis of axillary lymph node tissue sections

Abstract

Recently Fourier Transform Infrared (FTIR) spectroscopic imaging has been used as a tool to detect the changes in cellular composition that may reflect the onset of a disease. This approach has been investigated as a mean of monitoring the change of the biochemical composition of cells and providing a diagnostic tool for various human cancers and other diseases. The discrimination between different types of tissue based upon spectroscopic data is often achieved using various multivariate clustering techniques. However, the number of clusters is a common unknown feature for the clustering methods, such as hierarchical cluster analysis, k-means and fuzzy c-means. In this study, we apply a FCM based clustering algorithm to obtain the best number of clusters as given by the minimum validity index value. This often results in an excessive number of clusters being created due to the complexity of this biochemical system. A novel method to automatically merge clusters was developed to try to address this problem. Three lymph node tissue sections were examined to evaluate our new method. These results showed that this approach can merge the clusters which have similar biochemistry. Consequently, the overall algorithm automatically identifies clusters that accurately match the main tissue types that are independently determined by the clinician.

Vibrational spectroscopy: a clinical tool for cancer diagnostics

“…71,72 The technology has also been used to study other complex biological materials such as tissues, body fluids and cell cultures. [73][74][75][76][77] Like RS, the ability of FTIR spectroscopy to probe biochemical and morphological characterisation of cells has enabled its use in distinguishing cells at different phases of their life cycle, stages of cell proliferation and further differentiate between normal and cancerous cells. [78][79][80] FTIR spectroscopic imaging of tissue microarrays in conjunction with fast analysis methods have been described as a concept for rapid, high-throughput spectroscopic analysis of tissue samples.…”

Automation of an algorithm based on fuzzy clustering for analyzing tumoral heterogeneity in human skin carcinoma tissue sections

“…The fuzziness index m was fixed to the commonly used default value of 2, according to investigations of other groups. 31 A total of 11 clusters were chosen as they allow an unequivocal reproduction of the H&E-based histology as previously described with 'hard' clusterings ( Figure 2). Each cluster is presented into a separate image instead of superimposing them into only one color-coded image as reported by others.…”

“…Each cluster is presented into a separate image instead of superimposing them into only one color-coded image as reported by others. 16,27,31 Indeed, the superimposition presentation makes it difficult to highlight transitional structures.…”

Data-Based Fuzzy TOPSIS for Alternative Ranking

“…Meanwhile, has to satisfy the following condition (7) (8) denotes the Euclidean distance between and . Fuzziness index used to control the fuzziness of membership of each datum (Yang, 2007).…”