Wavelet based segmentation of hyperspectral colon tissue imagery

Segmentation is an early stage for the automated classification of tissue cells between normal and malignant types. We present an algorithm for unsupervised segmentation of images of hyperspectral human colon tissue cells into their constituent parts by exploiting the spatial relationship between these constituent parts. This is done by employing a modification of the conventional wavelet based texture analysis, on the projection of hyperspectral image data in the first principal component direction. Results show that our algorithm is comparable to other more computationally intensive methods which exploit the spectral characteristics of the hyperspectral imagery data

Hyperspectral colon tissue cell classification

A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

Hubungan gaya pembelajaran dengan pencapaian akademik pelajar aliran vokasional

Analisis keputusan Sijil Pelajaran Malaysia (SPM) 2011 menunjukkan penurunan pencapaian bagi Sekolah Menengah Vokasional. Oleh itu, kajian ini dilaksanakan bertujuan untuk mengkaji hubungan di antara gaya pembelajaran dengan pencapaian akademik pelajar. Kajian ini juga ingin mengenalpasti gaya pembelajaran paling dominan yang diamalkan oleh pelajar serta melihat perbezaan gaya pembelajaran dengan jantina pelajar. Seramai 131 orang Pelajar Tingkatan Empat Kursus Vokasional Di Sekolah Menengah Vokasional Segamat di Johor telah terlibat dalam kajian ini. Soal selidik Index of Learning Style (ILS) yang dibangunkan oleh Felder dan Silverman (1991) yang mengandungi 44 soalan telah digunakan untukh menjalankan kajian ini. Gaya pembelajaran pelajar dapat dilihat melalui empat dimensi gaya pembelajaran yang terdiri dari dua sub-skala yang bertentangan iaitu dimensi pelajar Aktif dan Reflektif, dimensi pelajar Konkrit dan Intuitif, dimensi pelajar Verbal dan Visual, serta dimensi pelajar Tersusun dan Global. Data yang diperolehi dianalisis dengan menggunakan perisian Statistical Package for Social Science for WINDOW release 20.0 (SPSS.20.0). Ujian Korelasi Pearson digunakan untuk menganalisis data dalam mengkaji hubungan gaya pembelajaran dengan pencapaian akademik pelajar. Nilai pekali p yang diperolehi di antara gaya pembelajaran dengan pencapaian pelajar adalah (p=0.1 hingga 0.4). Ini menunjukkan tidak terdapat hubungan yang signifikan di antara dua pembolehubah tersebut. Kajian ini juga mendapati bahawa gaya pembelajaran yang menjadi amalan pelajar ialah gaya pembelajaran Tersusun. Hasil kajian juga mendapati bahawa tidak terdapat perbezaan yang signifikan di antara gaya pembelajaran dengan jantina pelajar

A new approach in solving illumination and facial expression problems for face recognition

In this paper, a novel dual optimal multiband features (DOMF) method is presented to increase the robustness of face recognition system to illumination and facial expression variations.The wavelet packet transform first decomposes image into low-, mid- and high-frequency subbands and the multiband feature fusion technique is incorporated to select the subbands that are invariant to illumination and expression variation separately.These subbands form the optimal feature sets.Parallel radial basis function neural networks are employed to classify these feature sets.The scores generated by the neural networks are combined by an adaptive fusion mechanism where the level of illumination variations of the testing image is estimated and the weights are assigned to the scores accordingly.The experimental results show that DOMF outperforms other algorithms and also achieves promising performance on illumination and facial expression variation conditions

Selection of Wavelet Subbands Using Genetic Algorithm for Face Recognition

Abstract. In this paper, a novel representation called the subband face is proposed for face recognition. The subband face is generated from selected subbands obtained using wavelet decomposition of the original face image. It is surmised that certain subbands contain information that is more significant for discriminating faces than other subbands. The problem of subband selection is cast as a combinatorial optimization problem and genetic algorithm (GA) is used to find the optimum subband combination by maximizing Fisher ratio of the training features. The performance of the GA selected subband face is evaluated using three face databases and compared with other wavelet-based representations.

A new approach to face recognition using Curvelet Transform

Multiresolution tools have been profusely employed in face recognition. Wavelet Transform is the best known among these multiresolution tools and is widely used for identification of human faces. Of late, following the success of wavelets a number of new multiresolution tools have been developed. Curvelet Transform is a recent addition to that list. It has better directional ability and effective curved edge representation capability. These two properties make curvelet transform a powerful weapon for extracting edge information from facial images. Our work aims at exploring the possibilities of curvelet transform for feature extraction from human faces in order to introduce a new alternative approach towards face recognition