Fuzzy technique for microcalcifications clustering in digital mammograms

Background Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control. Methods In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from target dimensions and to optimize the recognition efficiency. A clustering method, based on a Fuzzy C-means (FCM), has been developed. The described method, Fuzzy C-means with Features (FCM-WF), was tested on simulated clusters of microcalcifications, implying that the location of the cluster within the breast and the exact number of microcalcifications are known.The proposed method has been also tested on a set of images from the mini-Mammographic database provided by Mammographic Image Analysis Society (MIAS) publicly available. Results The comparison between FCM-WF and standard FCM algorithms, applied on both databases, shows that the former produces better microcalcifications associations for clustering than the latter: with respect to the private and the public database we had a performance improvement of 10% and 5% with regard to the Merit Figure and a 22% and a 10% of reduction of false positives potentially identified in the images, both to the benefit of the FCM-WF. The method was also evaluated in terms of Sensitivity (93% and 82%), Accuracy (95% and 94%), FP/image (4% for both database) and Precision (62% and 65%). Conclusions Thanks to the private database and to the informations contained in it regarding every single microcalcification, we tested the developed clustering method with great accuracy. In particular we verified that 70% of the injected clusters of the private database remained unaffected if the reconstruction is performed with the FCM-WF. Testing the method on the MIAS databases allowed also to verify the segmentation properties of the algorithm, showing that 80% of pathological clusters remained unaffected

A Fuzzy Logic C-Means Clustering Algorithm to Enhance Microcalcifications Clusters in Digital Mammograms

The detection of microcalcifications is a hard task, since they are quite small and often poorly contrasted against the background of images. The Computer Aided Detection (CAD) systems could be very useful for breast cancer control. In this paper, we report a method to enhance microcalcifications cluster in digital mammograms. A Fuzzy Logic clustering algorithm with a set of features is used for clustering microcalcifications. The method described was tested on simulated clusters of microcalcifications, so that the location of the cluster within the breast and the exact number of microcalcifications is known

Automated approach for indirect immunofluorescence images classification based on unsupervised clustering method

Autoimmune diseases (ADs) are a collection of many complex disorders of unknown aetiology resulting in immune responses to self-antigens and are thought to result from interactions between genetic and environmental factors. ADs collectively are amongst the most prevalent diseases in the U.S., affecting at least 7% of the population. The diagnosis of ADs is very complex, the standard screening methods provides seeking and recognizing of Antinuclear Antibodies (ANA) by Indirect ImmunoFluorescence (IIF) based on HEp-2 cells. In this paper an automatic system able to identify and classify the Centromere pattern is presented. The method is based on the grouping of centromeres present on the cells through a clustering K-means algorithm. The performances were obtained on two public database of IIF images (A.I.D.A. and MIVIA). Our results showed a sensitivity for image of (90 \ub1 5)% and a Accuracy equal to (98.0 \ub1 0.5)%. Results demonstrate that the system is able to identify and classify Centromere pattern with accuracy better or comparable with some representative state of the art works. Moreover, it should be noted that for the classification phase the works used for the comparison used an expert-manual segmentation while, in the present work, the segmentation was obtained automatically

Unsupervised clustering method for pattern recognition in IIF images

Autoimmune diseases are a family of more than 80 chronic, and often disabling, illnesses that develop when underlying defects in the immune system lead the body to attack its own organs, tissues, and cells. Diagnosis of autoimmune pathologies is based on research and identification of antinuclear antibodies (ANA) through indirect immunofluorescence (IIF) method and is performed by analyzing patterns and fluorescence intensity. We propose here a method to automatically classify the centromere pattern based on the grouping of centromeres on the cells through a clustering K-means algorithm. The described method was tested on a public database (MIVIA). The results of the test showed an Accuracy equal to (92.0 ± 1.0)%. Comparing our results with the results obtained on the MIVIA database it is possible to note that our method has a performance comparable with the three best values obtained. Indeed, the method here proposed allows an automatic segmentation and counting of the cells in the images, while the participants to the contest received the training set with the original images of the cells already segmented by specialists

Lung Biomolecular Profile and Function of Grafts from Donors after Cardiocirculatory Death with Prolonged Donor Warm Ischemia Time

The acceptable duration of donor warm ischemia time (DWIT) after cardiocirculatory death (DCD) is still debated. We analyzed the biomolecular profile and function during ex vivo lung perfusion (EVLP) of DCD lungs and their correlation with lung transplantation (LuTx) outcomes. Donor data, procurement times, recipient outcomes, and graft function up to 1 year after LuTx were collected. During EVLP, the parameters of graft function and metabolism, perfusate samples to quantify inflammation, glycocalyx breakdown products, coagulation, and endothelial activation markers were obtained. Data were compared to a cohort of extended-criteria donors after brain death (EC-DBD). Eight DBD and seven DCD grafts transplanted after EVLP were analyzed. DCD’s DWIT was 201 [188;247] minutes. Donors differed only regarding the duration of mechanical ventilation that was longer in the EC-DBD group. No difference was observed in lung graft function during EVLP. At reperfusion, “wash-out” of inflammatory cells and microthrombi was predominant in DCD grafts. Perfusate biomolecular profile demonstrated marked endothelial activation, characterized by the presence of inflammatory mediators and glycocalyx breakdown products both in DCD and EC-DBD grafts. Early graft function after LuTx was similar between DCD and EC-DBD. DCD lungs exposed to prolonged DWIT represent a potential resource for donation if properly preserved and evaluated