Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images

Background To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. Methods in the current work, entropy based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results the segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be as accurate when only trained by 8 GLCMs. Conclusion The research illustrated the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Adaptive smoothness constraint image multilevel fuzzy enhancement algorithm

For the problems of poor enhancement effect and long time consuming of the traditional algorithm, an adaptive smoothness constraint image multilevel fuzzy enhancement algorithm based on secondary color-to-grayscale conversion is proposed. By using fuzzy set theory and generalized fuzzy set theory, a new linear generalized fuzzy operator transformation is carried out to obtain a new linear generalized fuzzy operator. By using linear generalized membership transformation and inverse transformation, secondary color-to-grayscale conversion of adaptive smoothness constraint image is performed. Combined with generalized fuzzy operator, the region contrast fuzzy enhancement of adaptive smoothness constraint image is realized, and image multilevel fuzzy enhancement is realized. Experimental results show that the fuzzy degree of the image is reduced by the improved algorithm, and the clarity of the adaptive smoothness constraint image is improved effectively. The time consuming is short, and it has some advantages

Defect Detection And Classification Of Silicon Solar Wafer Featuring Nir Imaging And Improved Niblack Segmentation

Menghasilkan tenaga yang boleh diperbaharui berkuantiti tinggi memerlukan kecekapan yang tinggi dalam fabrikasi produk wafer silikon, yang juga merupakan komponen asas panel solar. Oleh yang demikian, pemeriksaan kualiti yang tinggi untuk wafer solar semasa proses pengeluaran sangat penting. Dalam tesis ini, sistem pengesanan kecacatan yang cekap dan automatik menggunakan strategi pengelasan dan kelompok termaju telah dicadangkan. Dalam kajian ini, satu skema mesin penglihatan untuk mengesan keretakan mikro dan kecacatan-kecacatan yang lain dalam pembuatan polihabluran dan mono kristal wafer solar dicadangkan dan dibangunkan. Pemeriksaan retak mikro sangat mencabar kerana kecacatan ini sangat kecil dan tidak boleh dilihat dengan mata kasar. Kewujudan struktur heterogenus yang lain dalam wafer solar seperti bahan-bahan kasar dan kawasan gelap menjadikan pemeriksaan lebih mencabar. Dalam tesis ini, sebuah inspektor retak mikro yang mengandungi pencahayaan inframerah yang dekat dan algoritma segmentasi Niblack yang diperbaharui telah dicadangkan. Keputusan emperikal dan visual menunjukkan ketepatan dan prestasi yang lebih baik dari segi angka merit Pratt dan kaedah penilaian yang lain berbanding dengan formula pengambangan Niblack yang sedia ada. Keputusan angka merit (FOM), ketepatan (ACC), pekali kesamaan dadu (DSC) dan sensitiviti yang masing-masingnya sentiasa lebih tinggi daripada 0.871, 99.35 %, 99.68 %, dan 99.75 % bagi imej-imej dalam kajian ini. Sementara itu, satu set deskriptor bersepadanan dengan penerangan ciri-ciri bentuk Fourier eliptik, diekstrak bagi setiap kecacatan yang telah dikesan, dan dinilai bagi setiap kluster bagi tujuan pengelompokan dan pengelasan. Pengelasan menggabungkan analisis ciri keamatan kecacatan, penggunaan tanpa pengawasan kelompok purata-k dan pelbagai kelas algoritma SVM. Kaedah-kaedah ini telah digunakan untuk pengesanan, pengelompokan dan klasifikasi imej wafer solar polihabluran, bersepadanan dengan kecacatan seperti keretakan mikro, kekotoran, dan cap jari. Keputusan kajian menunjukkan bahawa kaedah purata-k dan penklasifikasi SVM mampu mengelompok dengan tepat kecacatan-kecacatan tersebut dengan ketepatan, indeks Rand, dan Bayang indeks dengan nilai purata masing-masing sebanyak 99.8 %, 99.788 %, dan 98.43 %. ________________________________________________________________________________________________________________________ Producing a high yield of renewable energy requires a high efficiency in product fabrication of silicon wafers, which is the basic building component of solar panels. For this reason, the high quality inspection of solar wafers during the procedures of production is very important. In this thesis, an automatic and efficient defect detection system, utilising advanced classification and clustering strategies are proposed. In this study a machine vision scheme for detecting micro-cracks and other defects in polycrystalline and monocrystalline solar wafer manufacturing is proposed and developed. Micro-crack inspection is very challenging, because this type of defect is very small and completely invisible to the naked eye. The presence of other heterogeneous structures in solar wafers like grainy materials and dark regions further complicates the problem. In this study an efficient micro-crack inspector comprising near infrared illumination and an improved Niblack segmentation algorithm is proposed. Empirical and visual results demonstrate that the proposed solutions are competitive when compared to existing Niblack thresholding formulas and other standard methods, and achieve better precision and performance in terms of Pratt’s figure of merit and other evaluation methods. Result in a figure of merit (FOM), accuracy (ACC), dice similarity coefficient (DSC), and sensitivity were consistently higher than 0.871, 99.35 %, 99.68 %, and 99.75 %, respectively, for all images tested in this study. Meanwhile, a set of descriptors corresponding to Elliptic Fourier Features shape description is extracted for each defect and is evaluated for each cluster to use for clustering and classification part. The classification combines the analysis of defect intensity features, the application of unsupervised k-mean clustering and multi-class SVM algorithms. The methods have been applied for detecting, clustering and classification polycrystalline solar wafer images, corresponding to defects such as micro cracks, stain, and fingerprints. Results indicate that the k-mean and SVM classifier can accurately cluster the defects with accuracy, Rand index, and Silhouette index averaging at 99.8 %, 99.788 %, and 98.43 %, respectively