Ekstraksi Fitur Citra Radiografi Thorax Menggunakan DWT dan Moment Invariant

COVID-19 is an infectious disease caused by thecoronavirus family, namely severe acute respiratorysyndrome coronavirus 2 (SARS-CoV-2). The fastest methodto identify the presence of this virus is a rapid antibody or antigen test, but confirming the positive status of a COVID-19 patient requires further examination. Lung examination using chest X-ray images taken through X-rays of COVID-19patients can be one way to confirm the patient's conditionbefore/after the rapid test. This paper proposes a featureextraction model to detect COVID-19 through chestradiography using a combination of Discrete WaveletTransform (DWT) and Moment Invariant features. In thiscase, haar wavelet transform and seven Hu moments wereused to extract image features in order to find unique featuresthat represent chest radiographic images as suspectedCOVID-19, pneumonia, or normal. To find out theuniqueness of the proposed features, it is coupled with thekNN and generic ANN classification techniques. Based on theperformance parameters assessed, it turns out that thewavelet-based and moment invariant thorax radiographicimage feature model can be used as a unique featureassociated with three categories: Normal, Pneumonia, andCovid-19. This is indicated by the accuracy value of 82.7% inthe kNN classification technique and the accuracy, precision,and recall of 86%, 87%, and 86% respectively with the ANNclassification technique.

Fusion of Moment Invariant Method and Deep Learning Algorithm for COVID-19 Classification

The COVID-19 pandemic has resulted in a global health crisis. The rapid spread of the virus has led to the infection of a significant population and millions of deaths worldwide. Therefore, the world is in urgent need of a fast and accurate COVID-19 screening. Numerous researchers have performed exceptionally well to design pioneering deep learning (DL) models for the automatic screening of COVID-19 based on computerised tomography (CT) scans; however, there is still a concern regarding the performance stability affected by tiny perturbations and structural changes in CT images. This paper proposes a fusion of a moment invariant (MI) method and a DL algorithm for feature extraction to address the instabilities in the existing COVID-19 classification models. The proposed method incorporates the MI-based features into the DL models using the cascade fusion method. It was found that the fusion of MI features with DL features has the potential to improve the sensitivity and accuracy of the COVID-19 classification. Based on the evaluation using the SARS-CoV-2 dataset, the fusion of VGG16 and Hu moments shows the best result with 90% sensitivity and 93% accuracy.

Detection of Collaterals from Cone-Beam CT Images in Stroke

Collateral vessels play an important role in the restoration of blood flow to the ischemic tissues of stroke patients, and the quality of collateral flow has major impact on reducing treatment delay and increasing the success rate of reperfusion. Due to high spatial resolution and rapid scan time, advance imaging using the cone-beam computed tomography (CBCT) is gaining more attention over the conventional angiography in acute stroke diagnosis. Detecting collateral vessels from CBCT images is a challenging task due to the presence of noises and artifacts, small-size and non-uniform structure of vessels. This paper presents a technique to objectively identify collateral vessels from non-collateral vessels. In our technique, several filters are used on the CBCT images of stroke patients to remove noises and artifacts, then multiscale top-hat transformation method is implemented on the pre-processed images to further enhance the vessels. Next, we applied three types of feature extraction methods which are gray level co-occurrence matrix (GLCM), moment invariant, and shape to explore which feature is best to classify the collateral vessels. These features are then used by the support vector machine (SVM), random forest, decision tree, and K-nearest neighbors (KNN) classifiers to classify vessels. Finally, the performance of these classifiers is evaluated in terms of accuracy, sensitivity, precision, recall, F-Measure, and area under the receiver operating characteristics curve. Our results show that all classifiers achieve promising classification accuracy above 90% and able to detect the collateral and non-collateral vessels from images.

Design of Image Copy-Paste Forensics System Based on Moment Invariants

Copy-paste tampering is a common type of digital image tampering, which refers to copying a part of the image area in the same image, and then pasting it into another area of the image to generate a forged image, so as to carry out malicious operations such as fraud and framing. This kind of malicious forgery leads to the security problem of digital image. The research of digital image copy paste forensics has important theoretical significance and practical value. For digital image copy-paste tampering, this paper is based on moment invariant image copy paste tampering detection algorithm, and use Matlab software to design the corresponding tampering forensics system.

Image Network Teaching Resource Retrieval Algorithm Based on Deep Hash Algorithm

In order to improve the retrieval ability of multiview attribute coded image network teaching resources, a retrieval algorithm of image network teaching resources based on depth hash algorithm is proposed. The pixel big data detection model of the multiview attribute coding image network teaching resources is constructed, the pixel information collected by the multiview attribute coding image network teaching resources is reconstructed, the fuzzy information feature components of the multiview attribute coding image are extracted, and the edge contour distribution image is combined. The distributed fusion result of the edge contour of the view image of the network teaching resources realizes the construction of the view feature parameter set. The gray moment invariant feature analysis method is used to realize information coding, the depth hash algorithm is used to realize the retrieval of multiview attribute coded image network teaching resources, and the information recombination is realized according to the hash coding result of multiview attribute coded image network teaching resources, thus improving the fusion. The simulation results show that this method has higher precision, better retrieval precision, and higher level of resource fusion for multiview coded image network teaching resource retrieval.

EKSTRASI CIRI WARNA HSV DAN CIRI BENTUK MOMENT INVARIANT UNTUK KLASIFIKASI BUAH APEL MERAH

Apel merah merupakan salah satu tanaman buah dengan banyak sekali peminat sehingga sangat laris di pasaran. Apel merah juga memiliki beberapa jenis yang sepintas terlihat mirip satu dengan yang lain. Hal inilah yang membuat orang kesulitan dalam membedakan apel merah yang dikonsumsi, apalagi tidak ada label keterangan untuk menjelaskan buah apel tersebut. Oleh karena itu, dalam penelitian ini dilakukan suatu klasifikasi terhadap buah apel merah berdasarkan ciri bentuk dan warna. Data citra yang digunakan yaitu data sekunder yang berformat *JPG dengan ukuran 100 x 100 piksel. Metode yang digunakan yaitu ekstrasi ciri warna Mean HSV (nilai outputnya 3) dan ciri bentuk Moment Invariant (nilai outputnya 7) sehingga setiap citra memiliki 10 nilai. Hasil klasifikasi citra diperoleh dengan menggunakan Euclidean Distance. Sedangkan, skenario pengujian digunakan K-Fold Cross Validation dimana 1.710 data citra dibagi kedalam 10-fold dengan setiap subset terdapat 171 citra. Dari 10-fold dilakukan pengujian sebanyak 50 kali, sehingga diperoleh rata-rata akurasi sebesar 98,82%. Untuk akurasi tertinggi diperoleh pada pengujian ke-46 sebesar 99,12% dan akurasi terendah pada pengujian ke-48 sebesar 98,54%.

Performance Evaluation of ANN based Hand Geometry Recognition

The research paper includes development of Application GUI for the ANN Hand Geometry based Recognition System with initial stages of Image Acquisition, Image Pre-processing and Feature Extraction and ANN Recognition using MATLAB. The application is to be tested on database for accuracy and performance and analytical comparisons are to be made on basis of testing. The research presents a method based on moment invariant method and Artificial Neural Network (ANN) which uses a four-step process: separates the hand image from its background, normalizes and digitizes the image, applies statistical features like Length and Width of the Fingers, Diameter of the Palm Perimeter Measurements, maxima and mini points and finally implements recognition and was successful in the verification as ANN was trained for seven neural net layers with 150000 iterations each. Neural network with MLP is highly efficient. The ANN is trained and tested on a total of 150 input palm images from CASIA Multi-Spectral Palmprint Image Database. The two different datasets are created for Left Palm Images and Right Palm Images. The Dataset1 includes 90 left palm images from 15 subjects with 06 images from each subject. The Dataset2 includes 60 right palm images from 10 subjects with 06 images from each subject.