Lung Tumor Segmentation Using Marker-Controlled Watershed and Support Vector Machine

The medical imaging technique showed remarkable improvement in interventional treatment of computer-aided medical diagnosis system. Image processing techniques are broadly applied in detection and exploring the abnormalities issues in tumor detection. The early stage of lung tumor detection is extremely important in medical research field. The proposed work uses image processing segmentation technique for detection of lung tumor and the support vector classifier learning technique for predicting stage of tumor. After performing preprocessing and segmentation the features are extracted from region of lung nodule. The classification is performed on dataset acquired from national cancer institute for the evaluation of lung cancer diagnosis. The multi-class machine learning classification technique SVM (support vector machine) identifies the tumor stage of lung dataset. The proposed methodology provides classification of tumor stages and improves the decision-making process. The performance is evaluated by measuring the parameters namely accuracy, sensitivity, and specificity.

Download Full-text

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

ACTA AGRONOMICA SINICA ◽

10.3724/sp.j.1006.2012.00374 ◽

2013 ◽

Vol 38 (2) ◽

pp. 374-379 ◽

Cited By ~ 3

Author(s):

Zhi-Li PAN ◽

Meng QI ◽

Chun-Yang WEI ◽

Feng LI ◽

Shi-Xiang ZHANG ◽

...

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

Support Vector ◽

Tobacco Leaves ◽

Region Classification

Download Full-text

Classification of Red Blood Cell Morphology Using Image Processing and Support Vector Machine

Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology ◽

10.1145/3397391.3397407 ◽

2020 ◽

Author(s):

Paul Daniel C. Divina ◽

John Philip T. Felices ◽

Carlos C. Hortinela ◽

Janette C. Fausto ◽

Flordeliza L. Valiente ◽

...

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

Blood Cell ◽

Cell Morphology ◽

Red Blood Cell ◽

Support Vector ◽

Blood Cell Morphology

Download Full-text

Lung Cancer Detection using Image Processing Techniques and its Classification

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38245 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1578-1583

Author(s):

Aishwarya .R

Keyword(s):

Lung Cancer ◽

Image Processing ◽

Support Vector Machine ◽

Cell Carcinoma ◽

Mortality Rates ◽

The Body ◽

Support Vector ◽

Lung Tumour ◽

Image Processing Techniques ◽

Processing Techniques

Abstract: Lung cancer has been a major contribution to mortality rates world-wide for many years now. There is a need for early diagnosis of lung cancer which if implemented, will help in reducing mortality rates. Recently, image processing techniques have been widely applied in various medical facilities for accurate detection and diagnosis of abnormality in the body images like in various cancers such as brain tumour, breast tumour and lung tumour. This paper is a development of an algorithm based on medical image processing to segment the lung tumour in CT images due to the lack of such algorithms and approaches used to detect tumours. The work involves the application of different image processing tools in order to arrive at the desired result when combined and successively applied. The segmentation system comprises different steps along the process. First, Image preprocessing is done where some enhancement is done to enhance and reduce noise in images. In the next step, the different parts in the images are separated to be able to segment the tumour. In this phase threshold value was selected automatically. Then morphological operation (Area opening) is implemented on the thresholded image. Finally, the lung tumour is accurately segmented by subtracting the opened image from the thresholded image. Support Vector Machine (SVM) classifier is used to classify the lung tumour into 4 different types: Adenocarcinoma(AC), Large Cell Carcinoma(LCC) Squamous Cell Carcinoma(SCC), and No tumour (NT). Keywords: Lung tumour; image processing techniques; segmentation; thresholding; image enhancement; Support Vector Machine; Machine learning;

Download Full-text

A Comparative Study of Support Vector Machine and Maximum Likelihood Classification to Extract Land Cover of Lahore District, Punjab, Pakistan

Pakistan Journal of Scientific & Industrial Research Series A: Physical Sciences ◽

10.52763/pjsir.phys.sci.64.3.2021.265.274 ◽

2021 ◽

Vol 64 (3) ◽

pp. 265-274

Author(s):

Fatima Mushtaq ◽

Khalid Mahmood ◽

Mohammad Chaudhry Hamid ◽

Rahat Tufail

Keyword(s):

Support Vector Machine ◽

Maximum Likelihood ◽

Land Cover ◽

Support Vector ◽

Kappa Statistics ◽

Error Matrix ◽

Classification Techniques ◽

Machine Learning Classification ◽

Hyper Plane ◽

Better Than

The advent of technological era, the scientists and researchers develop machine learning classification techniques to classify land cover accurately. Researches prove that these classification techniques perform better than previous traditional techniques. In this research main objective is to identify suitable land cover classification method to extract land cover information of Lahore district. Two supervised classification techniques i.e., Maximum Likelihood Classifier (MLC) (based on neighbourhood function) and Support Vector Machine (SVM) (based on optimal hyper-plane function) are compared by using Sentinel-2 data. For this optimization, four land cover classes have been selected. Field based training samples have been collected and prepared through a survey of the study area at four spatial levels. Accuracy for each of the classifier has been assessed using error matrix and kappa statistics. Results show that SVM performs better than MLC. Overall accuracies of SVM and MLC are 95.20% and 88.80% whereas their kappa co-efficient are 0.93 and 0.84 respectively.

Download Full-text

SURF Points Versus SIFT Points in Identification of Medicinal Plants

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a9466.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 602-607

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

Medicinal Plants ◽

Digital Image Processing ◽

Digital Image ◽

Support Vector ◽

Scale Invariant ◽

Invariant Feature ◽

Feature Transform ◽

Scale Invariant Feature

Today, digital image processing is used in diverse fields; this paper attempts to compare the outcome of two commonly used techniques namely Speeded Up Robust Feature (SURF) points and Scale Invariant Feature Transform (SIFT) points in image processing operations. This study focuses on leaf veins for identification of plants. An algorithm sequence has been utilized for the purpose of recognition of leaves. SURF and SIFT extractions are applied to define and distinguish the limited structures of the documented vein image of the leaf separately and Support Vector Machine (SVM) is integrated to classify and identify the correct plant. The results prove that the SURF algorithm is the fastest and an efficient one. The results of the study can be extrapolated to authenticate medicinal plants which is the starting step to standardize herbs and carryout research.

Download Full-text

DESAIN APLIKASI PENGENALAN POLA TANDA TANGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)

JURNAL MEDIA INFOTAMA ◽

10.37676/jmi.v17i1.1311 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Gunawan Gunawan ◽

Yuza Reswan

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

Credit Card ◽

Support Vector

Penggunaan tanda tangan saat ini banyak digunakan untuk memverifikasi keabsahan dari berbagai transaksi keuangan. Lembar cek, credit card dan berbagai dokumen lainnya menggunakan tanda tangan sebagai pengenal keabsahan seseorang. Teknologi identifikasi untuk pengenalan pola tanda tangan termasuk didalam biometrika yang menggunakan karakteristik perilaku alami manusia. pada penelitian ini akan dibuat suatu perancangan aplikasi pengenalan pola tanda tangan menggunakan metode Support Vector Machine. Tujuan yang ingin dicapai adalah untuk membangun sistem yang dapat mendeteksi tipe pemalsuan tanda tangan berbasis metode Support Vector Machine. Kesimpulan dari penelitian ini memberikan kemudahan dalam melakukan pengenalan terhadap pola tanda tangan seseorang tersebut sehingga dapat diketahui informasi pemilik tanda tangan tersebut dan mendapatkan data tentang pola tanda tangan. Rancangan aplikasi ini yang dapat membantu pengguna dalam upaya melakukan deteksi tanda tangan, rancangan ini layak digunakan sehingga dapat memberikan kemudahan dalam melakukan pengenalan terhadap pola tanda tangan seseorang tersebut sehingga dapat diketahui informasi pemilik tanda tangan tersebut. Hasil olah Matlab pada image processing dan jaringan syaraf tiruan biasanya disimpan pada ekstensi *.mat karena variabel image processing dan jaringan syaraf tiruan bersifat angka. Kata Kunci : Aplikasi, Tanda Tangan, SVM, Pola

Download Full-text

Automatic Soil Classification by using Gabor Wavelet & Support Vector Machine in Digital Image Processing

10.1109/icirca51532.2021.9544897 ◽

2021 ◽

Author(s):

Shraddha Shivhare ◽

Kanchan Cecil

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

Digital Image Processing ◽

Digital Image ◽

Soil Classification ◽

Gabor Wavelet ◽

Support Vector

Download Full-text

Enhancement in Quality Estimation of Resistance Spot Welding Using Vision System and Fuzzy Support Vector Machine

Symmetry ◽

10.3390/sym12081380 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1380

Author(s):

Dima Younes ◽

Essa Alghannam ◽

Yuegang Tan ◽

Hong Lu

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

Real Time ◽

Resistance Spot Welding ◽

Spot Welding ◽

Failure Modes ◽

Support Vector ◽

Fuzzy Support Vector Machine ◽

Quality Estimation ◽

Resistance Spot

The current nondestructive testing methods such as ultrasonic, magnetic, or eddy current signals, and even the existing image processing methods, present certain challenges and show a lack of flexibility in building an effective and real-time quality estimation system of the resistance spot welding (RSW). This paper provides a significant improvement in the theory and practices for designing a robotized inspection station for RSW at the car manufacturing plants using image processing and fuzzy support vector machine (FSVM). The weld nuggets’ positions on each of the used car underbody models are detected mathematically. Then, to collect perfect pictures of the weld nuggets on each of these models, the required end-effector path is planned in real-time by establishing the Denavit-Hartenberg (D-H) model and solving the forward and inverse kinematics models of the used six-degrees of freedom (6-DOF) robotic arm. After that, the most frequent resistance spot-welding failure modes are reviewed. Improved image processing methods are employed to extract new features from the elliptical-shaped weld nugget’s surface and obtain a three-dimensional (3D) reconstruction model of the weld’s surface. The extracted artificial data of thousands of samples of the weld nuggets are divided into three groups. Then, the FSVM learning algorithm is formed by applying the fuzzy membership functions to each group. The improved image processing with the proposed FSVM method shows good performance in classifying the failure modes and dealing with the image noise. The experimental results show that the improvement of comprehensive automatic real-time quality evaluation of RSW surfaces is meaningful: the quality estimation could be processed within 0.5 s in very high accuracy.

Download Full-text