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Classification of retinal images based on convolutional neural network

MICROSCOPY RESEARCH AND TECHNIQUE 2021年第3期84卷 394-414页

作者： El-Hag, Noha A. Sedik, Ahmed El-Shafai, Walid El-Hoseny, Heba M. Khalaf, Ashraf A. M. El-Fishawy, Adel S. Al-Nuaimy, Waleed Abd El-Samie, Fathi E. El-Banby, Ghada M. Minia Univ Fac Engn Dept Elect & Elect Comm Al Minya Egypt Kafrelshiekh Univ Fac Artificial Intelligent Dept Robot & Intelligent Machines Kafr Al Sheikh Egypt Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia Egypt Al Obour High Inst Engn & Technol Dept Elect & Elect Commun Engn Al Qalyubia Governorate Egypt Univ Liverpool Dept Elect & Elect Engn Liverpool Merseyside England Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 21974 Saudi Arabia Menoufia Univ Fac Elect Engn Dept Ind Elect & Control Engn Menoufia Egypt

Automatic detection of maculopathy disease is a very important step to achieve high-accuracy results for the early discovery of the disease to help ophthalmologists to treat patients. Manual detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates from retinal images is applied for the maculopathy disease diagnosis. The first proposed framework in this paper for retinal image classification begins with fuzzy preprocessing in order to improve the original image to enhance the contrast between the objects and the background. After that, image segmentation is performed through binarization of the image to extract both blood vessels and the optic disc and then remove them from the original image. A gradient process is performed on the retinal image after this removal process for discrimination between normal and abnormal cases. Histogram of gradients is estimated, and consequently the cumulative histogram of gradients is obtained and compared with a threshold cumulative histogram at certain bins. To determine the threshold cumulative histogram, cumulative histograms of images with exudates and images without exudates are obtained and averaged for each type, and the threshold cumulative histogram is set as the average of both cumulative histograms. Certain histogram bins are selected and thresholded according to the estimated threshold cumulative histogram, and the results are used for retinal image classification. In the second framework in this paper, a Convolutional Neural Network (CNN) is utilized to classify normal and abnormal cases.

关键词： CNN contrast enhancement diabetic maculopathy exudate detection fuzzy enhancement Gaussian gradient retinal images segmentation process

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segmentation of Urea Melt Marbles and Application of One-Shot segmentation in Batch Production of Large Urea Granules

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ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2021年第43期9卷 14430-14442页

作者： Huang, Chunni Wu, Pan Liu, Shuyuan He, Yunyi He, Jian Liu, Changjun Jiang, Wei Sichuan Univ Sch Chem Engn Low Carbon Technol & Chem React Engn Lab Chengdu 610065 Peoples R China

Steel strip granulation is a promising technology for preparing large urea granules;however, easy blocking of the feeding nozzle by urea melt limits its industrial applications. In this study, a dust-free and anticlogging urea granulation process for the mass production of large urea granules is proposed by introducing a urea melt marble (UMM) formed by covering urea melt with super-urea-phobic poly(tetrafluoroethylene) (PTFE) particles to improve steel strip granulation. Large urea granules are obtained by directly cutting a large-sized cake-shaped mother-UMM into segments. These segments shrink into spherical baby-UMMs and solidify after condensation to form rigid particles. The results confirm that the PTFE powder distribution density for constructing a stable mother-UMM by the segmentation process is crucial. The PTFE powder distribution density for obtaining usable baby-UMMs and subsequent qualified large urea granules should be within 0.0009-0.0011 g/cm(2). Super-urea-phobic honeycomb-shaped cutters are used for the batch preparation of qualified urea products with adequate quality distribution. This study provides a promising strategy for improving the production of urea granules.

关键词： urea melt marbles segmentation process mechanical strength large urea granule super-urea-phobic surface

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Hybrid wavelet denoising procedure of discontinuous surfaces

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IET IMAGE processING 2011年第8期5卷 684-692页

作者： Kim, D. Oh, H-S Naveau, P. Sejong Univ Dept Appl Math Seoul 143747 South Korea Seoul Natl Univ Dept Stat Seoul 151747 South Korea Lab Sci Climat & Environm F-91191 Gif Sur Yvette France

In this study, the authors propose a method for recovering a two-dimensional surface (image) from noisy observations containing significant jumps and discontinuities. The proposed procedure, termed as the segmented polynomial wavelet regression (SPWR) algorithm, combines wavelet regression with polynomial extrapolation and segmentation procedures. The bias that might occur on the discontinuous surface is alleviated through a segmentation process, and at the same time, inhomogeneous multi-scale features of the surface are efficiently treated by wavelet regression. The SPWR algorithm enjoys all the benefits of wavelet regression by implicitly detecting the discontinuities, and it is fast and easy to implement. Through a simulation study, it is demonstrated that the proposed method can produce substantially effective results.

关键词： polynomial extrapolation two-dimensional image recovery segmented polynomial wavelet regression algorithm segmentation process SPWR algorithm regression analysis noisy observations Computer vision and image processing techniques hybrid wavelet denoising procedure extrapolation Optical, image and video signal processing inhomogeneous multiscale features image segmentation wavelet transforms feature extraction Interpolation and function approximation (numerical analysis) discontinuous surfaces image denoising Integral transforms in numerical analysis two-dimensional surface recovery Other topics in statistics

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A NEW METHODOLOGY TO SOLVE THE PROBLEM OF CHARACTERIZING 2-D BIOMEDICAL SHAPES

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 1995年第3期46卷 187-205页

作者： FDEZVALDIVIA, J GARCIA, JA DELABLANCA, NP GARRIDO, A FUERTES, JM UNIV JENA ESCUELA POLITEC SUPERDEPT INFORMATE-23071 JAENSPAIN

In this paper, a new approach to solve the problem of characterizing 2-D biomedical shapes is introduced. Two-dimensional biomedical contours are described through a 'degrees of smoothing' vector in which each component determines the proper degree of detail for representing each curve part isolating a single structure. A segmentation process is designed based on a clustering procedure applied to vectors of texture measures which are obtained from the graph of curvatures. To solve the problem of characterizing biomedical shapes, a suitable interpolation procedure between the most outstanding perceptual points from the smoothed contours is given.

关键词： 2-DIMENSIONAL BIOMED CONTOUR DEGREES OF SMOOTHING VECTOR segmentation process CLUSTERING PROCEDURE CURVATURE PERCEPTUAL POINT

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Optimised hybrid classifiers for automatic HEp-2 cell classification

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IET IMAGE processING 2020年第16期14卷 4316-4328页

作者： Chariyamparambil Chandran, Manju Victor Jose, Marianthiran Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kumaracoil 629180 TN India Noorul Islam Ctr Higher Educ Dept MCA Kumaracoil 629180 TN India

This paper introduces a new HEp-2 cell classification model under two phases. In the initial phase, the input image is segmented using the morphological operations (opening and closing), and the segmented image is given for Convolutional Neural Network (CNN) classifier that gives the classified output. In the second phase, the given input is processed under (i) segmentation process (ii) Feature Extraction, and (iii) Classification. From the segmented images;the features like Gray level co-occurrence Matrix TGLCM) and Gray level Run Length Matrix (GLRM) are extracted. After extracting the features, they are subjected to a classification process, where Neural Network (NN) is used. Finally, the mean of both classified output (first phase and second phase) is considered to be the final classified output. As the main contribution, to enhance the classification accuracy, the hidden neurons of both classifiers (CNN and NN) are optimally chosen during the classification process. To make this possible, this paper aims to propose a new Randomized Update based Grey Wolf Optimization (RP-GWO) algorithm. Finally, the performance of the implemented approach is compared over other conventional approaches and its superiority is proven with respect to certain measures.

关键词： medical image processing image segmentation image classification cellular biophysics feature extraction neural nets image texture diseases image representation improved diagnosis ability huge intra-class deviations HEp-2 cell classification model input image segmented image convolutional neural network classifier segmentation process co-occurrence matrix grey-level run-length matrix classification process final classified output classification accuracy randomised update-based grey wolf optimisation algorithm optimised hybrid classifiers automatic HEp-2 cell classification human epithelial type 2 cell image classifications computer-aided diagnosis

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Efficient approach for non-ideal iris segmentation using improved particle swarm optimisation-based multilevel thresholding and geodesic active contours

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IET IMAGE processING 2018年第10期12卷 1721-1729页

作者： Rapaka, Satish Kumar, Pullakura Rajesh Sir CR Reddy Coll Engn Dept Elect & Commun Engn Eluru 534007 Andhra Prades India Andhra Univ Coll Engn Dept Elect & Commun Engn Visakhapatnam 530003 Andhra Prades India

segmentation is an important step in iris recognition framework because the accuracy of the iris recognition system is affected by the segmentation of the iris. The image acquisition introduces noise artefacts such as specular reflections, eyelids/eyelashes occlusions and overlapping intensities, which makes the segmentation process difficult. An efficient method has been proposed for the segmentation of iris images that deal with non-circular iris boundaries and other noise artefacts mentioned above. The proposed method uses the Otsu multilevel thresholding based on improved particle swarm optimisation technique as a pre-segmentation step. Pre-segmentation step delimits the iris region from the other parts of an eye image. The geodesic active contours incorporated with a novel stopping function is then used to segment non-circular iris boundaries. The recognition accuracy of the proposed method is verified using the standard databases, CASIA v3 Interval and UBIRISv1. Obtained results have been compared with existing methods and have an encouraging performance.

关键词： particle swarm optimisation image recognition feature extraction medical image processing image segmentation iris recognition efficient approach nonideal iris segmentation geodesic active contours iris recognition framework iris recognition system image acquisition noise artefacts eyelids eyelashes occlusions overlapping intensities segmentation process iris images Otsu multilevel thresholding improved particle swarm optimisation technique pre-segmentation step iris region eye image segment noncircular iris boundaries recognition accuracy

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An MRI-based deep learning approach for efficient classification of brain tumors

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第6期14卷 6697-6718页

作者： Ul Haq, Ejaz Huang Jianjun Kang Li Ul Haq, Hafeez Tijiang Zhang Shenzhen Univ Sch Elect & Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen Peoples R China Fujian Normal Univ Fuzhou Peoples R China Zunyi Med Univ Imaging Dept Zunyi Guizhou Peoples R China

Efficient and reliable identification and classification of brain tumors from imaging data is essential in the diagnosis and treatment of brain cancer cells. Magnetic resonance imaging (MRI) is the most commonly used imaging modality in the analysis of infected brain tissue. However, manual segmentation requires significant time to process data produced by magnetic resonance imaging. In this study, we present two fast and proficient brain tumor identification techniques based on deep convolutional neural networks (CNNs) using magnetic resonance imaging data for the effective detection and classification of different types of brain tumors. We use two publicly available datasets from Figshare and BraTS 2018, and apply conditional random fields to eliminate forged outputs, considering spatial information on fine segmentation tasks. The first proposed architecture, based on the Figshare dataset, classifies brain tumors as gliomas, meningiomas, or pituitary tumors. The second architecture differentiates between high- and low-grade gliomas (HGG and LGG, respectively). An intensity normalization method is also investigated as a pre-processing step, which proves highly effective at detection and classification of brain tumors in combination with data augmentation techniques. The Figshare and BraTS 2018 datasets included 3062 and 251 images, respectively. The experimental results demonstrate an accuracy of 97.3% and a dice similarity coefficient (DSC) 95.8% on the task of classifying brain tumor as gliomas, meningiomas, or pituitary tumors achieved by the first proposed CNN architecture, while second proposed CNN architecture achieved an accuracy of 96.5% with a DSC of 94.3% on the task of classifying glioma grades as HGG or LGG. Experimental results reveal that our proposed model attained improved performance and increased classification accuracy compared to state-of-the-art methods.

关键词： segmentation process Brain tumor Deep convolutional neural network Data augmentation Magnetic resonance images

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Lung Lesion Classification Scheme Using Optimization Techniques and Hybrid (KNN-SVM) Classifier

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IETE JOURNAL OF RESEARCH 2022年第2期68卷 1485-1499页

作者： Rani, K. Vijila Jawhar, S. Joseph Anna Univ Informat & Commun Engn Chennai Tamil Nadu India Arunachala Coll Engn Women Nagercoil Tamil Nadu India

This paper proposes an intellectual classification system to classify normal and abnormal CT scan lung images. Prediction of cancer cells from lung image is such a difficult task for physicians and researchers. A novel technique for lung tumour detection with Grey Wolf Optimized & Whale Optimization Algorithm - Support Vector Machine (GWO & WOA-SVM) is proposed. CT scan images are used as input images. Investigations focused on the segmentation and classification part to find the lung lesion region. For finding the lung abnormality and lesion region in the lung, four different stages are used. The first stage is the image acquisition process. The second stage is the image pre-processing and enhancement stage, here Advanced Clustering (AC) technique is used. The next stage is the segmentation process, here the advanced surface normal overlap (ASNO) lung segmentation algorithm is used. Finally, the lung lesion is classified using the hybrid classifier followed by a different optimization technique. The research gap for this paper is to find the lung lesion using an optimization-based hybrid classification technique. Nowadays, classification decision and treatment of lung tumours are based on symptoms and radiological appearance. Here Hybrid Classifier (KNN-SVM) is used to classify 700 images;it is observed from the results that the Hybrid classifier KNN-SVM demonstrated the highest classification accuracy rate of 97.6% among other methods.

关键词： Lung tumour Advanced Clustering segmentation process ASNO Hybrid KNN-SVM Classifier Grey Wolf Optimized Whale Optimization Algorithm

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AUTOMATIC CHARACTERIZATION OF SPIRAL AND ELLIPTIC GALAXIES FROM DIGITAL IMAGES

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PATTERN RECOGNITION LETTERS 1994年第9期15卷 861-869页

作者： GARCIA, JA MOLINA, R DELABLANCA, NP UNIV GRANADA DEPT CIENCIAS COMP & IAE-18071 GRANADASPAIN

In this paper we describe the application of pattern recognition techniques to the astronomical problem of the automatic characterization of galaxies. The characterization problem is reduced to the study of the boundary of a region obtained from a segmentation process on the digital image. We show that there is enough information to characterize an elliptical or spiral galaxy from the information on the boundary of this region. This boundary exhibits deformations due to the arms on spiral galaxies, but shows a nearly elliptical shape on elliptical galaxies. Based on the information provided by this curve, we then study and compare three different galaxy characterization methods.

关键词： segmentation process SMOOTHING process IMAGE MODEL SPIRAL AND ELLIPTIC GALAXIES GALAXY CHARACTERIZATION COMPACTNESS, GRAPH OF CURVATURE

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Review of advanced computational approaches on multiple sclerosis segmentation and classification

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IET SIGNAL processING 2020年第6期14卷 333-341页

作者： Shanmuganathan, Manimurugan Almutairi, Saad Aborokbah, Majed Mohammed Ganesan, Subramaniam Ramachandran, Varatharajan Univ Tabuk Fac Comp & Informat Technol Tabuk City Saudi Arabia Univ Tabuk Fac Comp & Informat Technol Ind Innovat & Robot Ctr Tabuk City Saudi Arabia Oakland Univ Dept Elect & Comp Engn Rochester MI 48063 USA Bharath Inst Higher Educ & Res Dept Elect & Commun Engn Chennai Tamil Nadu India

In this study, a survey of multiple sclerosis (MS) classification and segmentation process is presented, which is based on magnetic resonance imaging. Knowledge of MS lesions is gained by determining the number of sample lesions in order that the lesion development level can be followed precisely;therefore, the effects of pharmaceuticals in medical tests can be accurately assessed. Accurate recognition of MS lesions in magnetic resonance images is an additionally complex process because of their changing shapes and sizes which can be very difficult to identify based on anatomical positions in various subjects. This can be determined by precise segmentation;manual segmentation would be very difficult to perform as it requires high level knowledge which takes additional time. Inter- and intra-expert variability need to be determined in order to perform the automated segmentation of lesions. The principal aim of this survey effort is to provide an analysis of the different categorization and segmentation methods and their techniques. This survey work will be valuable for researchers working in MS by considering and carefully evaluating the past work. The benefits and drawbacks of existing techniques are reviewed and the issue of MS lesion segmentation and classification is elucidated.

关键词： medical image processing biomedical MRI neurophysiology image segmentation brain classification methods survey work MS lesion segmentation multiple sclerosis segmentation multiple sclerosis classification segmentation process magnetic resonance imaging MS lesions sample lesions lesion development level magnetic resonance images precise segmentation manual segmentation automated segmentation

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