Binarization of Ancient Malayalam Documents - A Novel Weight-based Denoising Approach

Even though several studies exist on denoising degraded documents, now a days it is a tedious task in the field of document image processing because ancient document may contain several degradations which will be a barrier for reader. Here we use old Malayalam Grantha scripts that contain useful information like the poem titled ‘Njana Stuthi’ and ancient literature. These historical documents are losing content due to heavy degradations such as, ink bleed, fungi-found to be brittleness & show through. In order to remove these kind of degradations, the study is proposing a novel binarization algorithm which remove noises from Grantha scripts as well as notebook images and make the document readable. Here we use 500 datasets of Grantha scripts for experimentation. In our proposed method, binarization is done through a channel based method in which we are converting image in to RGB, further adding weights to make the image darker or brighter followed by morphological operation open and finally passing it RGB and HSV channel for more clarity and clear separation of black text and white background, remaining noise will be removed using adaptive thresholding technique. The proposed method is outperformed with good accuracy.

Small intestine bleeding detection using color threshold and morphological operation in WCE images

<span>Wireless capsule endoscopy (WCE) is a significant modern technique for observing the whole gastroenterological tract to diagnose various diseases like bleeding, ulcer, tumor, Crohn's disease, polyps etc in a non-invasive manner. However, it will make a substantial onus for physicians like human oversight errors with time consumption for manual checking of a vast amount of image frames. These problems motivate the researchers to employ a computer-aided system to classify the particular information from the image frames. Therefore, a computer-aided system based on the color threshold and morphological operation has been proposed in this research to recognize specified bleeding images from the WCE. Besides, A unique classifier, quadratic support vector machine (QSVM) has been employed for classifying the bleeding and non-bleeding images with the statistical feature vector in HSV color space. After extensive experiments on clinical data, 95.8% accuracy, 95% sensitivity, 97% specificity, 80% precision, 99% negative predicted value and 85% F1 score has been achieved, which outperforms some of the existing methods in this regard. It is expected that this methodology would bring a significant contribution to the WCE technology. </span>

Brain Tumor Detection and Segmentation Using Watershed Segmentation and Morphological Operation

In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region.

A Novel Pre-Processing Approach for the Denoising of Alzheimer Disease Image Dataset

Medical imaging doing an indispensable part in the area of medicine. Noise in the image is maddening as it worsens the quality of image. Thus, removal of noise is perpetually a problematic work in the images of all domain. Alzheimer’s disease, a neurological dysfunction in which destruction of cells in brain creates mental weakening and memory loss. The distinguished reason for Alzheimer’s disease is low brain activity and low blood flow. We proposed a framework for removal of noise in Alzheimer disease image using histogram equalization, thresholding, open morphological operation and a wavelet transform. This framework reduces the noise and significantly better than the existing methods used for Alzheimer disease images.

Identifikasi Kematangan Cabai Menggunakan Operasi Morfologi (Opening dan Closing) dan Metode Backpropagation

AbstrakIdentifikasi kualitas buah cabai biasanya masih menggunakan cara visual secara langsung atau sortir secara manual oleh petani, dengan menggunakan sistem ini sering kali terjadi beberapa kesalahan setiap melakukan sortir yang disebabkan oleh petani yang melakukan sortir merasa terlalu lelah. Dengan menggunakan komputasi pengolahan citra digital, untuk melakukan identifikasi pengelompokan buah cabai yang matang dan mentah dapat membantu para petani, Teknik pengelompokan ini akan menggunakan metode pengelompokan berdasarkan warna. Metode pengelompokan tersebut sebelumnya akan dilakukan operasi morfologi pada citra yang telah diambil. Pendekatan operasi morfologi pada penelitian ini adalah Opening and Closing, pada operasi morfologi akan menghilangkan noise dan menebalkan objek dari inputan gambar. Metode Bacpropagatioan akan mengolah data latih sebanyak 10 data latih mendapatkan 6 iterasi perhitungan dan setelah diuji menggunakan data uji hasil yang didapatkan yaitu tingkat pengenalan rata-rat mendapatkan perhitungan sebanyak 7 iterasi metode Bacpropagation. Hasil dari penelitian ini juga dihitung menggunakan Confusion Matrix dimana nilai Precision 90%, Recall 74%, dan Accuracy 70%, maka dapat disimpulkan bahwa Operasi Morfologi dan Metode Backpropagation dapat digunakan untuk mengidentifikasi objek cabai.Kata Kunci: backpropagation, morfologi, identifikasi, opening and closing AbstractIdentification of the quality of chili fruit is usually still using a visual way directly or sorting manually by farmers, using this system often occurs several errors, every sorting caused by farmers who do the sorting feel too tired. By using digital image processing computing, to identify the grouping of ripe and raw chili fruits can help farmers, this grouping technique will use a method of grouping based on color. The grouping method will previously perform morphological surgery on the image that has been taken. The morphological operation approach in this study is Opening and Closing, in morphological operations will eliminate noise and thicken objects from image input. Bacpropagatioan method will process training data as much as 10 training data get 6 iterations of calculations and after being tested using the test data obtained results that is the level of introduction of the average rat get a calculation of 7 iterations bacpropagation method. The results of this study were also calculated using Confusion Matrix where precision values of 90%, Recall 74%, and Accuracy 70%, it can be concluded that Morphological Operations and Backpropagation Method can be used to identify chili objects.Keywords: backpropagation, morfologi, identification, opening and closing

Texton Based Segmentation for Road Defect Detection from Aerial Imagery

Road defect such as potholes and road cracks, became a problem that arose every year in Indonesia. It could endanger drivers and damage the vehicles. It also obstructed the goods distribution via land transportation that had major impact to the economy. To handle this problem, the government released an online complaints system that utilized information system and GPS technology. To follow up the complaints especially road defect problem, a survey was conducted to assess the damage. Manual survey became less effective for large road area and might disturb the traffic. Therefore, we used road aerial imagery captured by Unmanned Aerial Vehicle (UAV). The proposed method used texton combined with K-Nearest Neighbor (K-NN) to segment the road area and Support Vector Machine (SVM) to detect the road defect. Morphological operation followed by blob analysis was performed to locate, measure, and determine the type of defect. The experiment showed that the proposed method able to segment the road area and detect road defect from aerial imagery with good Boundary F1 score.