A Fractional-order Sobel Operator for Medical Image Structure Feature Extraction

This paper proposes a novel fractional-order gradient operator for medical image structure feature extraction. The proposed operator can be seen as generalization of the first-order Sobel operator. The goal is to utilize the frequency characteristic of the fractional derivative for extracting more structure feature details. A thresholding is set based on the average fractional-order gradient for marking the edge points, and then the image structure can be extracted. Experiments show that the proposed fractional-order operator yields good visual effects.

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A fractional-order edge detection operator for medical image structure feature extraction

The 26th Chinese Control and Decision Conference (2014 CCDC) ◽

10.1109/ccdc.2014.6853103 ◽

2014 ◽

Cited By ~ 8

Author(s):

Dan Tian ◽

Jingfei Wu ◽

Yajie Yang

Keyword(s):

Feature Extraction ◽

Edge Detection ◽

Fractional Order ◽

Medical Image ◽

Image Structure ◽

Structure Feature

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A Fractional-Order Laplacian Operator for Image Edge Detection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.55 ◽

2014 ◽

Vol 536-537 ◽

pp. 55-58 ◽

Cited By ~ 3

Author(s):

Dan Tian ◽

Jing Fei Wu ◽

Ya Jie Yang

Keyword(s):

Edge Detection ◽

Fractional Derivative ◽

Fractional Order ◽

Second Order ◽

Laplacian Operator ◽

Order Operator ◽

Visual Effects ◽

Image Edge Detection ◽

Edge Points ◽

Image Edge

This paper proposes a novel fractional-order Laplacian operator for image edge detection. The proposed operator can be seen as generalization of the second-order Laplacian operator. The goal is to utilize the global characteristic of the fractional derivative for extracting more edge details. A thresholding is set based on the average fractional-order gradient for marking the edge points, and then the image edge can be extracted. Experiments show that the proposed fractional-order operator yields good visual effects.

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Kinetics and Mechanism of Hexachloroiridate(IV) Oxidation of Arsenic(III) in Acidic Perchlorate Solutions

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19921451 ◽

1992 ◽

Vol 57 (7) ◽

pp. 1451-1458 ◽

Cited By ~ 6

Author(s):

Refat M. Hassan

Keyword(s):

Ionic Strength ◽

Fractional Order ◽

Activation Parameters ◽

Order Reaction ◽

First Order Reaction ◽

Intermediate Complex ◽

Constant Ionic Strength ◽

Kinetics Of Oxidation ◽

First Order ◽

Kinetics Of

The kinetics of oxidation of arsenic(III) by hexachloroiridate(IV) at lower acid concentrations and at constant ionic strength of 1.0 mol dm-3 have been investigated spectrophotometrically. A first-order reaction in [IrCl62-] and fractional order with respect to arsenic(III) have been observed. A kinetic evidence for the formation of an intermediate complex between the hydrolyzed arsenic(III) species and the oxidant was presented. The results showed that decreasing the [H+] is accompanied by an appreciable acceleration of the rate of oxidation. The activation parameters have been evaluated and a mechanism consistent with the kinetic results was suggested.

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A Densely Connected Network Based on U-Net for Medical Image Segmentation

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3446618 ◽

2021 ◽

Vol 17 (3) ◽

pp. 1-14

Author(s):

Zhenzhen Yang ◽

Pengfei Xu ◽

Yongpeng Yang ◽

Bing-Kun Bao

Keyword(s):

Feature Extraction ◽

Image Segmentation ◽

Loss Function ◽

Network Architecture ◽

Medical Image ◽

Medical Image Segmentation ◽

Cross Entropy ◽

Loss Functions ◽

Feature Maps ◽

Different Levels

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.

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On the Strong Stability of First-Order Operator-Differential Equations

Differential Equations ◽

10.1023/b:dieq.0000043528.60501.e9 ◽

2004 ◽

Vol 40 (5) ◽

pp. 703-710 ◽

Cited By ~ 1

Author(s):

D. R. Bojovic ◽

B. S. Jovanovic ◽

P. P. Matus

Keyword(s):

Differential Equations ◽

Strong Stability ◽

Order Operator ◽

First Order

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Feature extraction for object recognition using PCA-KNN with application to medical image analysis

2013 36th International Conference on Telecommunications and Signal Processing (TSP) ◽

10.1109/tsp.2013.6614055 ◽

2013 ◽

Cited By ~ 1

Author(s):

Patrik Kamencay ◽

Robert Hudec ◽

Miroslav Benco ◽

Martina Zachariasova

Keyword(s):

Image Analysis ◽

Feature Extraction ◽

Object Recognition ◽

Medical Image ◽

Medical Image Analysis

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Formulae of partial reduction for linear systems of first order operator equations

Applied Mathematics Letters ◽

10.1016/j.aml.2010.06.033 ◽

2010 ◽

Vol 23 (11) ◽

pp. 1367-1371 ◽

Cited By ~ 1

Author(s):

Branko Malešević ◽

Dragana Todorić ◽

Ivana Jovović ◽

Sonja Telebaković

Keyword(s):

Linear Systems ◽

Partial Reduction ◽

Order Operator ◽

Operator Equations ◽

First Order

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Penerapan K-Means Clustering Untuk Seleksi Frame Dominan Berbasis NTSC Pada Obyek Bergerak

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020742184 ◽

2020 ◽

Vol 7 (4) ◽

pp. 745

Author(s):

Rizka Indah Armianti ◽

Achmad Fanany Onnilita Gaffar ◽

Arief Bramanto Wicaksono Putra

Keyword(s):

Feature Extraction ◽

Video Data ◽

Clustering Method ◽

Data Frame ◽

First Order ◽

Feature Extraction And Selection ◽

Different Shapes ◽

Frame Set ◽

Selection Of

Obyek dinyatakan bergerak jika terjadi perubahan posisi dimensi disetiap frame. Pergerakan obyek menyebabkan obyek memiliki perbedaan bentuk pola disetiap frame-nya. Frame yang memiliki pola terbaik diantara frame lainnya disebut frame dominan. Penelitian ini bertujuan untuk menyeleksi frame dominan dari rangkaian frame dengan menerapkan metode K-means clustering untuk memperoleh centroid dominan (centroid dengan nilai tertinggi) yang digunakan sebagai dasar seleksi frame dominan. Dalam menyeleksi frame dominan terdapat 4 tahapan utama yaitu akuisisi data, penetapan pola obyek, ekstrasi ciri dan seleksi. Data yang digunakan berupa data video yang kemudian dilakukan proses penetapan pola obyek menggunakan operasi pengolahan citra digital, dengan hasil proses berupa pola obyek RGB yang kemudian dilakukan ekstraksi ciri berbasis NTSC dengan menggunakan metode statistik orde pertama yaitu Mean. Data hasil ekstraksi ciri berjumlah 93 data frame yang selanjutnya dikelompokkan menjadi 3 cluster menggunakan metode K-Means. Dari hasil clustering, centroid dominan terletak pada cluster 3 dengan nilai centroid 0.0177 dan terdiri dari 41 data frame. Selanjutnya diukur jarak kedekatan seluruh data cluster 3 terhadap centroid, data yang memiliki jarak terdekat dengan centroid itulah frame dominan. Hasil seleksi frame dominan ditunjukkan pada jarak antar centroid dengan anggota cluster, dimana dari seluruh 41 data frame tiga jarak terbaik diperoleh adalah 0.0008 dan dua jarak bernilai 0.0010 yang dimiliki oleh frame ke-59, ke-36 dan ke-35. AbstractThe object is declared moving if there is a change in the position of the dimensions in each frame. The movement of an object causes the object to have different shapes in each frame. The frame that has the best pattern among other frames is called the dominant frame. This study aims to select the dominant frame from the frame set by applying the K-means clustering method to obtain the dominant centroid (the highest value centroid) which is used as the basis for the selection of dominant frames. In selecting dominant frames, there are 4 main stages, namely data acquisition, determination of object patterns, feature extraction and selection. The data used in the form of video data which is then carried out the process of determining the pattern of objects using digital image processing operations, with the results of the process in the form of an RGB object pattern which is then performed NTSC-based feature extraction using the first-order statistical method, Mean. The data from feature extraction are 93 data frames which are then grouped into 3 clusters using the K-Means method. From the results of clustering, the dominant centroid is located in cluster 3 with a centroid value of 0.0177 and consists of 41 data frames. Furthermore, the proximity of all data cluster 3 to the centroid is measured, the data having the closest distance to the centroid is the dominant frame. The results of dominant frame selection are shown in the distance between centroids and cluster members, where from all 41 data frames the three best distances obtained are 0.0008, 0.0010, and 0.0010 owned by 59th, 36th and 35th frames.

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