The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Lung Segmentation is the first and important image processing step for the detection and the diagnosis of lung cancer, namely CADe and CADx respectively. In this paper, a new Lung Segmentation method is introduced which performs faster than any other existing methods without compromising the accuracy of the segmentation.
The measurement of residual thyroid tissue after thyroidectomy is crucial for the precise quantification of thyroid cancer treatment. Accurate residual thyroid tissue segmentation from CT images is challenging due to the indistinct tissue boundary. We propose a vote-in & vote-out region propagation model for residual thyroid tissue segmentation which incorporates global and local constraints and...
Deep learning methods for image analysis have shown impressive performance in recent years. In this paper, we present deep learning based approaches to solve two problems in skin lesion analysis using a dermoscopic image containing skin tumor. In the first problem, we use a fully convolutional-deconvolutional architecture to automatically segment skin tumor from the surrounding skin. In the second...
Automated segmentation of cell nuclei is crucial for the early diagnosis of cancer as the characteristics of the cell nuclei are mainly associated with the assessment of malignancy. Only a few research work has been done on automated segmentation of cell nuclei on cytology pleural effusion images, which is poorly handled by previous methods. In addition, cytology pleural effusion image itself is still...
This paper presents a study of the impact of image preprocessing techniques on the segmentation and classification of breast lesions on ultrasound. Commonly, image preprocessing performs contrast enhancement and speckle reduction. In this sense, five contrast enhancement techniques and four despeckling methods were combined to generate 20 different image preprocessing schemes. The experiments considered...
Ultrasound imaging and Fine Needle Aspiration Biopsy, which used for diagnosis of thyroid cancer, don't ensure sufficient sensitivity and specificity. Due to this fact, many patients undergo unnecessary thyroid removal. In this study, it is aimed to find new features based on non-invasive Computerized Tomography. 52 nodular goiter patients who underwent thyroid removal surgery with suspicion of cancer...
Lung cancer is a disease that caused by uncontrolled cell growth in lung. Lung cancer is still the first worldwide killer. CT Scan Thorax is a method for early detection of lung cancer patients. However, cancer detection in lung CT-Scan image still done manually. In this paper, the segmentation of lung image is proposed. Cancer segmentation will process the lung CT-Scan as an image input with watershed...
The asymmetry of skin lesion is one of the three-point checklist (3PCLD). The 3CPLD is depending of a shape, hue/color and structure of the lesion. In the paper, a dermatological asymmetry measure in hue (DASMHue) is presented and discussed. The hue distribution asymmetry of the segmented skin lesion is discussed and new dermatological asymmetry measures of hue distribution are defined. One of the...
The proposed work describes an effective pipeline for skin lesion (nevus) analysis with related oncological outcomes. The increasing statistics of skin cancer have recently contributed to the development of new methods for early detection and discrimination of malignant skin lesions in order to drastically reduce the number of biopsies often very invasive for the patients. The main aggressive skin...
Melanoma is certainly the deadliest skin cancer. Clinicians try to detect melanoma at early stages in order to increase the successful treatment rate by using dermoscopes. We have designed a digital dermoscope that is both mobile and highly sensitive for automatic classification. We developed an accurate image processing software and a learning program that uses artificial neural network learning...
The automated segmentation of cells in microscopic images is an open research problem that has important implications for studies of the developmental and cancer processes based on in vitro models. In this paper, we present the approach for segmentation of the DIC images of cultured cells using G-neighbor smoothing followed by Kauwahara filtering and local standard deviation approach for boundary...
Detection of a lung nodule in a chest CT favors in understanding the malignant behavior of nodules. The size of lung nodule which reflects the malignant nature helps in early diagnosis and treatment of lung cancer. False detection of lung nodule will misinterpret healthy patient as lung cancer patient which may lead to wrong medication by the clinician. Existing algorithms for detection of lung nodule...
Computer Aided Diagnostic (CAD) tools for differentiating benign and malignant lesions are primarily of great importance. Most of the CAD tools employ a large and complex feature set. In this paper, a CAD system for classifying benign and malignant lesions using optimal feature set is proposed. The optimal feature set included the prominent color, shape and texture features. The feature set used is...
Detection of pulmonary nodules has played a significant role in lung cancer diagnosis because nodules are the first suspicious symptoms for the likelihood of cancer. Margin characteristics of the pulmonary nodules provide essential radiological features to determine the possibility of malignancy. In general, benign nodules hold quite smooth margins whilst malignant ones hold irregular margins. The...
Cancer is the major reason for mortality worldwide. The chances of recovery can be well improved if it is possible to diagnose the cancer at its early stages. Cancer detection is conventionally done by invasive procedures like biopsy, but it causes lot of discomforts for the patient. Here Hyperspectral Images (HSI) based noninvasive alternative for biopsy is introduced, it is also applicable as a...
Detection and segmentation of cells is an important step for classifying the cells as cancerous or non-cancerous. Pathologists use microscopic images for analysis and further diagnosis of cancer. These images contain the microscopic structure of tissues and are stained using some staining components to facilitate the process. Staining process varies due to different stain manufacturers, staining practices...
This paper explores the feasibility of using multiframe analysis to increase the classification performance of machine learning methods for cancer detection in Volumetric Laser Endomicroscopy (VLE). VLE is a novel and promising modality for the detection of neoplasia in patients with Baretts Esophagus (BE). It produces hundreds of high-resolution, cross-sectional images of the esophagus and offers...
Melanoma is the most dangerous type of skin cancer, but when treated in its early stages the chance of cure is increased. However, the detection of melanoma is a challenging task even for specialists due to low contrast of skin lesions and presence of artifacts. Therefore, developing an automatic segmentation tool for skin lesion analysis using dermoscopy images is a critical step for improving the...
The proposed system aims to detect the lung nodules from a series of CT scan images. Otsu's thresholding and morphological operations are applied for nodules segmentation. After segmentation, the objects that do not hold the possibility to be nodules are removed. Geometric, histogram as well as texture features are then extracted for benign and malignant nodules classification. Multilayer Perceptron...
In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse tissue component characteristics and architectural properties to obtain enhanced image representations,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.