Effective fuzzy c-means based kernel function in segmenting medical images

An Intuitionistic Fuzzy Clustering Approach for Detection of Abnormal Regions in Mammogram Images

Breast cancer is one of the leading causes of mortality in the world and it occurs in high frequency among women that carries away many lives. To detect cancer, extraction or segmentation of lesions/tumors is required. Segmentation process is very crucial if the mammogram images are blurred or low contrast. This paper suggests a novel clustering approach for segmenting lesions/tumors in the mammogram images using Atanassov's intuitionistic fuzzy set theory. The algorithm initially converts an image to an intuitionistic fuzzy image using a novel intuitionistic fuzzy generator. From the intuitionistic fuzzy image, two membership intervals are computed. Then, using Zadeh's min t-conorm, a new membership function is computed. Using the new membership function, an interval type 2 fuzzy image is constructed. Two types of distance functions are used in clustering-intuitionistic fuzzy divergence and a fuzzy exponential type distance function. Further, in each iteration, membership matrix is updated using a hesitation degree and a clustered image is obtained. Tumors/lesions are then segmented from the clustered image. The proposed method is compared with existing methods both quantitatively and qualitatively and it is observed that the proposed method performs better than the existing methods.

An expanding manifold in transmodal regions characterizes adolescent reconfiguration of structural connectome organization

Adolescence is a critical time for the continued maturation of brain networks. Here, we assessed structural connectome development in a large longitudinal sample ranging from childhood to young adulthood. By projecting high-dimensional connectomes into compact manifold spaces, we identified a marked expansion of structural connectomes, with strongest effects in transmodal regions during adolescence. Findings reflected increased within-module connectivity together with increased segregation, indicating increasing differentiation of higher-order association networks from the rest of the brain. Projection of subcortico-cortical connectivity patterns into these manifolds showed parallel alterations in pathways centered on the caudate and thalamus. Connectome findings were contextualized via spatial transcriptome association analysis, highlighting genes enriched in cortex, thalamus, and striatum. Statistical learning of cortical and subcortical manifold features at baseline and their maturational change predicted measures of intelligence at follow-up. Our findings demonstrate that connectome manifold learning can bridge the conceptual and empirical gaps between macroscale network reconfigurations, microscale processes, and cognitive outcomes in adolescent development.

A neutrosophic-entropy based clustering algorithm (NEBCA) with HSV color system: A special application in segmentation of Parkinson's disease (PD) MR images

BACKGROUND AND OBJECTIVES

Brain MR images consist of three major regions: gray matter, white matter and cerebrospinal fluid. Medical experts make decisions on different serious diseases by evaluating the developments in these areas. One of the significant approaches used in analyzing the MR images were segmenting the regions. However, their segmentation suffers from two major problems as: (a) the boundaries of their gray matter and white matter regions are ambiguous in nature, and (b) their regions are formed with unclear inhomogeneous gray structures. For these reasons, diagnosis of critical diseases is often very difficult.

METHODS

This study presented a new method for MR image segmentation, which consisted of two main parts as: (a) neutrosophic-entropy based clustering algorithm (NEBCA), and (b) HSV color system. The NEBCA's role in this study was to perform segmentation of MR regions, while HSV color system was used to provide better visual representation of features in segmented regions.

RESULTS

Application of the proposed method was demonstrated in 30 different MR images of Parkinson's disease (PD). Experimental results were presented individually for the NEBCA and HSV color system. The performance of the proposed method was evaluated in terms of statistical metrics used in an image segmentation domain. Experimental results, including statistical analysis reflected the efficiency of the proposed method over the existing well-known image segmentation methods available in literature. For the proposed method and existing methods, the average CPU time (in nanosecond) was computed and it was found that the proposed method consumed less time to segment MR images.

CONCLUSION

The proposed method can effectively segment different regions of MR images and can very clearly represent those segmented regions.

Functional connectivity based parcellation of early visual cortices

Human brain can be divided into multiple brain regions based on anatomical and functional properties. Recent studies showed that resting-state connectivity can be utilized for parcellating brain regions and identifying their distinctive roles. In this study, we aimed to parcellate the primary and secondary visual cortices (V1 and V2) into several subregions based on functional connectivity and to examine the functional characteristics of each subregion. We used resting-state data from a research database and also acquired resting-state data with retinotopy results from a local site. The long-range connectivity profile and three different algorithms (i.e., K-means, Gaussian mixture model distribution, and Ward's clustering algorithms) were adopted for the parcellation. We compared the parcellation results within V1 and V2 with the eccentric map in retinotopy. We found that the boundaries between subregions within V1 and V2 were located in the parafovea, indicating that the anterior and posterior subregions within V1 and V2 corresponded to peripheral and central visual field representations, respectively. Next, we computed correlations between each subregion within V1 and V2 and intermediate and high-order regions in ventral and dorsal visual pathways. We found that the anterior subregions of V1 and V2 were strongly associated with regions in the dorsal stream (V3A and inferior parietal gyrus), whereas the posterior subregions of V1 and V2 were highly related to regions in the ventral stream (V4v and inferior temporal gyrus). Our findings suggest that the anterior and posterior subregions of V1 and V2, parcellated based on functional connectivity, may have distinct functional properties.

Connectivity-based parcellation: Critique and implications

Regional specialization and functional integration are often viewed as two fundamental principles of human brain organization. They are closely intertwined because each functionally specialized brain region is probably characterized by a distinct set of long-range connections. This notion has prompted the quickly developing family of connectivity-based parcellation (CBP) methods in neuroimaging research. CBP assumes that there is a latent structure of parcels in a region of interest (ROI). First, connectivity strengths are computed to other parts of the brain for each voxel/vertex within the ROI. These features are then used to identify functionally distinct groups of ROI voxels/vertices. CBP enjoys increasing popularity for the in-vivo mapping of regional specialization in the human brain. Due to the requirements of different applications and datasets, CBP has diverged into a heterogeneous family of methods. This broad overview critically discusses the current state as well as the commonalities and idiosyncrasies of the main CBP methods. We target frequent concerns faced by novices and veterans to provide a reference for the investigation and review of CBP studies.

Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm

Background

Organ segmentation is an important step in computer-aided diagnosis and pathology detection. Accurate kidney segmentation in abdominal computed tomography (CT) sequences is an essential and crucial task for surgical planning and navigation in kidney tumor ablation. However, kidney segmentation in CT is a substantially challenging work because the intensity values of kidney parenchyma are similar to those of adjacent structures.

Results

In this paper, a coarse-to-fine method was applied to segment kidney from CT images, which consists two stages including rough segmentation and refined segmentation. The rough segmentation is based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm and the refined segmentation is implemented with improved GrowCut (IGC) algorithm. The SKFCM algorithm introduces a kernel function and spatial constraint into fuzzy c-means clustering (FCM) algorithm. The IGC algorithm makes good use of the continuity of CT sequences in space which can automatically generate the seed labels and improve the efficiency of segmentation. The experimental results performed on the whole dataset of abdominal CT images have shown that the proposed method is accurate and efficient. The method provides a sensitivity of 95.46% with specificity of 99.82% and performs better than other related methods.

Conclusions

Our method achieves high accuracy in kidney segmentation and considerably reduces the time and labor required for contour delineation. In addition, the method can be expanded to 3D segmentation directly without modification.

Fractal dimension analysis of cerebellum in Chiari Malformation type I

Chiari Malformation type I (CM-I) is a serious neurological disorder that is characterized by hindbrain herniation. Our aim was to evaluate the usefulness of fractal analysis in CM-I patients. To examine the morphological complexity features of this disorder, fractal dimension (FD) of cerebellar regions were estimated from magnetic resonance images (MRI) of 17 patients with CM-I and 16 healthy control subjects in this study. The areas of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) were calculated and the corresponding FD values were computed using a 2D box-counting method in both groups. The results indicated that CM-I patients had significantly higher (p<0.05) FD values of GM, WM and CSF tissues compared to control group. According to the results of correlation analysis between FD values and the corresponding area values, FD and area values of GM tissues in the patients group were found to be correlated. The results of the present study suggest that FD values of cerebellar regions may be a discriminative feature and a useful marker for investigation of abnormalities in the cerebellum of CM-I patients. Further studies to explore the changes in cerebellar regions with the help of 3D FD analysis and volumetric calculations should be performed as a future work.

Similarity maps and hierarchical clustering for annotating FT-IR spectral images

Background

Unsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization.

Results

We introduce so-called interactive similarity maps as an alternative annotation strategy for annotating infrared microscopic images. We demonstrate that segmentations obtained from interactive similarity maps lead to similarly accurate segmentations as segmentations obtained from conventionally used hierarchical clustering approaches. In order to perform this comparison on quantitative grounds, we provide a scheme that allows to identify non-horizontal cuts in dendrograms. This yields a validation scheme for hierarchical clustering approaches commonly used in infrared microscopy.

Conclusions

We demonstrate that interactive similarity maps may identify more accurate segmentations than hierarchical clustering based approaches, and thus are a viable and due to their interactive nature attractive alternative to hierarchical clustering. Our validation scheme furthermore shows that performance of hierarchical two-means is comparable to the traditionally used Ward’s clustering. As the former is much more efficient in time and memory, our results suggest another less resource demanding alternative for annotating large spectral images.

A multiscale and multiblock fuzzy C-means classification method for brain MR images

PURPOSE

Classification of magnetic resonance (MR) images has many clinical and research applications. Because of multiple factors such as noise, intensity inhomogeneity, and partial volume effects, MR image classification can be challenging. Noise in MRI can cause the classified regions to become disconnected. Partial volume effects make the assignment of a single class to one region difficult. Because of intensity inhomogeneity, the intensity of the same tissue can vary with respect to the location of the tissue within the same image. The conventional "hard" classification method restricts each pixel exclusively to one class and often results in crisp results. Fuzzy C-mean (FCM) classification or "soft" segmentation has been extensively applied to MR images, in which pixels are partially classified into multiple classes using varying memberships to the classes. Standard FCM, however, is sensitive to noise and cannot effectively compensate for intensity inhomogeneities. This paper presents a method to obtain accurate MR brain classification using a modified multiscale and multiblock FCM.

METHODS

An automatic, multiscale and multiblock fuzzy C-means (MsbFCM) classification method with MR intensity correction is presented in this paper. We use a bilateral filter to process MR images and to build a multiscale image series by increasing the standard deviation of spatial function and by reducing the standard deviation of range function. At each scale, we separate the image into multiple blocks and for every block a multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels in order to overcome the effect of intensity inhomogeneity. The result from a coarse scale supervises the classification in the next fine scale. The classification method is tested with noisy MR images with intensity inhomogeneity.

RESULTS

Our method was compared with the conventional FCM, a modified FCM (MFCM) and multiscale FCM (MsFCM) method. Validation studies were performed on synthesized images with various contrasts, on the simulated brain MR database, and on real MR images. Our MsbFCM method consistently performed better than the conventional FCM, MFCM, and MsFCM methods. The MsbFCM method achieved an overlap ratio of 91% or higher. Experimental results using real MR images demonstrate the effectiveness of the proposed method. Our MsbFCM classification method is accurate and robust for various MR images.

CONCLUSIONS

As our classification method did not assume a Gaussian distribution of tissue intensity, it could be used on other image data for tissue classification and quantification. The automatic classification method can provide a useful quantification tool in neuroimaging and other applications.

Fuzzy logic: A "simple" solution for complexities in neurosciences?

Background:

Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.

Methods:

This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.

Results:

The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.

Conclusions:

In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences.