Self-organizing Tree Map Research Articles

Automatic assessment of medication states of patients with Parkinson's disease using wearable sensors.

Motor fluctuations are a major focus of clinical managements in patients with mid-stage and advance Parkinson's disease (PD). In this paper, we develop a new patient-specific algorithm that can classify those fluctuations during a variety of activities. We extract a set of temporal and spectral features from the ambulatory signals and then introduce a semi-supervised classification algorithm based on K-means and self-organizing tree map clustering methods. Two different types of cluster labeling are introduced: hard and fuzzy labeling. The developed algorithm is evaluated on a dataset from triaxial gyroscope sensors for 12 PD patients. The average result of using K-means and fuzzy labeling on the trunk and the more affected leg sensors' readings was 75.96%, 70.57%, and 86.93% for accuracy, sensitivity, and specificity, respectively. The accuracy for individual patients varied from 99.95% to 42.53%, which was correlated with dyskinesia severity and the improvement of the PD symptoms with medication.

An Adaptive Growing Self-Organizing Tree Map for Brain MR Image Segmentation

Segmentation of brain tissues from magnetic resonance (MR) images plays a crucial role in medical image processing. In this paper, we propose an automatic unsupervised segmentation method integrating wavelet transform with self-organizing map for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of wavelet transform and spatial position information. Then, an adaptive growing self-organizing tree map (AGSOTM) is presented, which adaptively captures the complicated spatial layout of the individual tissues, and overcomes the problem of overlapping grey-scale intensities for different tissues. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with other algorithms.

Strategies for unsupervised multimedia processing

In this article, we explore a new family of neural network architectures that have a basis in self-organization, yet are somewhat free from many of the constraints typical of other well-known self-organizing architectures. Within this family, the basic processing unit is known as the self-organizing tree map (SOTM). We will look at how this model has evolved since its inception in 1995, how it has inspired new models, and how it is being applied to complex multimedia research problems in digital asset management and microbiological image analysis.

Automatic machine interactions for content-based image retrieval using a self-organizing tree map architecture

In this paper, an unsupervised learning network is explored to incorporate a self-learning capability into image retrieval systems. Our proposal is a new attempt to automate recursive content-based image retrieval. The adoption of a self-organizing tree map (SOTM) is introduced, to minimize the user participation in an effort to automate interactive retrieval. The automatic learning mode has been applied to optimize the relevance feedback (RF) method and the single radial basis function-based RF method. In addition, a semiautomatic version is proposed to support retrieval with different user subjectivities. Image similarity is evaluated by a nonlinear model, which performs discrimination based on local analysis. Experimental results show robust and accurate performance by the proposed method, as compared with conventional noninteractive content-based image retrieval (CBIR) systems and user controlled interactive systems, when applied to image retrieval in compressed and uncompressed image databases.

Self-organizing tree map for eliminating impulse noise with random intensity distributions

A new method is proposed to eliminate impulse noise with a random intensity distribution in digital images. The method is based on impulse noise detection by means of a self-organizing tree map (SOTM) and a class of noise-exclusive adaptive filters. The SOTM classifies the image pixels into clusters and locates the cluster centers that represent the mean intensities of the impulse noise. Based on the detected impulse noise, the noise-exclusive filters are applied only to the corrupted pixels, using the true neighborhood information to estimate the pixel values. The filtering scheme presented can suppress impulse noise effectively while preserving image edges and fine details. Experimental results demonstrate that the performance of the noise-exclusive adaptive filters are superior to that of the traditional median filter family.