scholarly journals Automated Kidney Stone Segmentation by Seed Pixel Region Growing Approach: Initial Implementation and Results

2018 ◽  
Vol 7 (4.15) ◽  
pp. 43 ◽  
Author(s):  
Sujata Navaratnam ◽  
Siti Fazilah ◽  
Valliappan Raman ◽  
Sundresan Perumal
Keyword(s):  
Kidney Stone ◽  
Clinical Decision Making ◽  
Early Stage ◽  
Texture Feature ◽  
Region Growing ◽  
Clinical Decision ◽  
Seed Region ◽  
Region Growing Segmentation ◽  
Aided Diagnosis ◽  
Seed Pixel

This research develop a computer aided diagnosis prototype for early detection of kidney stone. Once a kidney stone is diagnosed accurately, this will be useful for the patients to change their diet condition. The proposed approach is based on five stages which includes kidney image acquisition, pre-processing, segmentation, feature extraction and classification. The enhanced seed region growing segmentation depends on the extracted feature granularities. Noise may be visible and more prevalent in certain dimensions of an image, where this particular specific portion will be extracted. The segmentation process is based on the thresholds of the identified renal stone regions. The segmented stone size portion is classified based on rules; if the size is greater than 2mm, then the stone is at benign stage; if the size is greater than 5mm, then it is in malignant stage; if the size is lesser than 2mm, then this leads to absence of stone. The proposed work is implemented in MATLAB with the development of an initial prototype with the detection of stone accuracy of 92%. Based on the experimental analysis, texture feature, threshold intensity values and stone sizes are evaluated. This study will help the urologist to take decision whether there is a presence or absence of stone in early stage diagnosis and clinical decision-making.  

Breast Tumor Detection in MR Images Based on Density

2021 ◽  
pp. 2250001
Author(s):  
Neeraj Shrivastava ◽  
Jyoti Bharti
Keyword(s):  
Breast Cancer ◽  
Breast Tumor ◽  
Early Stage ◽  
Region Growing ◽  
Tumor Detection ◽  
Misclassification Rate ◽  
Seed Region ◽  
True Negative ◽  
Multiple Tumor ◽  
Breast Image

Breast cancer is dangerous in women. It is generally found after the symptoms appear. Detecting the breast cancer at an early stage and understanding the treatment are the most important strategies to prevent death from cancer. Generally, for detection of breast cancer, breast Magnetic Resonance Image (MRI) takes place. It is one of the best approaches to detect tumor in women. In this research paper, a combination of selection methods for seed region growing image segmentation is suggested to detect breast tumor. The suggested method has been divided into following parts: First, the pre-processing of breast image is performed. Second, the automatic threshold for binarization process is calculated. Third, the number of seed points and its position in the breast image are determined automatically using density of pixels value. Fourth, a method for calculation of threshold value is proposed for the purpose of region creation in seed region growing. For the evaluation purpose, the proposed method was applied and tested on the RIDER MRI breast dataset from National Biomedical Imaging Archive (NBIA). After the test was performed, it was observed that proposed algorithm gives 90% accuracy, 88% True Negative Fraction, 91% True Positive Fraction, 10% Misclassification Rate, 94% Precision and 86% Relative Overlap which is better than other existing methods. It not only gives better evaluation measure but also provides segmentation method for multiple tumor detection.

Relevance Feedback as New Tool for Computer-Aided Diagnosis in Image Databases

2012 ◽  
pp. 86-106
Author(s):  
Issam El Naqa ◽  
Jung Hun Oh ◽  
Yongyi Yang
Keyword(s):  
Relevance Feedback ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Query Process ◽  
Large Databases ◽  
Computer Aided ◽  
High Level ◽  
Aided Diagnosis

With the ever-growing volume of images used in medicine, the capability to retrieve relevant images from large databases is becoming increasingly important. Despite the recent progress made in the field, its applications in Computer-Aided Diagnosis (CAD) thus far have been limited by the ability to determine the intrinsic mapping between high-level user perception and the underlying low-level image features. Relevance Feedback (RFB) is a post-query process to refine the search by using positive and/or negative indications from the user about the relevance of retrieved images, which has been applied successfully in traditional text-retrieval systems for improving the results of a retrieval strategy. In this chapter, the authors review some recent advances in RFB technology, and discuss its expanding role in content-based image retrieval from medical archives. They provide working examples, based on their experience, for developing machine-learning methods for RFB in mammography and highlight the potential opportunities in this field for CAD applications and clinical decision-making.

scholarly journals Treatment selection of early stage non-small cell lung cancer: the role of the patient in clinical decision making

BMC Cancer ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
S. Mokhles ◽  
J. J. M. E. Nuyttens ◽  
M. de Mol ◽  
J. G. J. V. Aerts ◽  
A. P. W. M. Maat ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Decision Making ◽  
Small Cell Lung Cancer ◽  
Clinical Decision Making ◽  
Early Stage ◽  
Clinical Decision ◽  
Small Cell ◽  
Small Cell Lung ◽  
Selection Of

scholarly journals A Nomogram-Based Model to Predict Neoplastic Risk For Patients With Gallbladder Polyps

2021 ◽  
Author(s):  
Xudong Zhang ◽  
Jin-Cheng Wang ◽  
Baoqiang Wu ◽  
Tao Li ◽  
Lei Jin ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Early Stage ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Training Group ◽  
Clinical Decision ◽  
Polyp Size ◽  
Training Cohort ◽  
Discrimination Ability ◽  
Gallbladder Polyps

Abstract Background: Gallbladder polyps (GBPs) assessment seeks to identify early-stage gallbladder carcinoma (GBC). Many studies have analyzed the risk factors for malignant GBPs, and we try to establish a more accurate predictive model for potential neoplastic polyps in patients with GBPs.Methods: This retrospective study developed a nomogram-based model in a training cohort of 233 GBP patients. Clinical information, ultrasonographic findings, and blood tests were retrospectively analyzed. Spearman correlation and logistic regression analysis were used to identify independent predictors and establish a nomogram model. An internal validation was conducted in 225 consecutive patients. Performance of models was evaluated through the receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Results: Age, cholelithiasis, CEA, polyp size and sessile were confirmed as independent predictors for neoplastic potential of GBPs in the training group. Compared with other proposed prediction methods, the established nomogram model presented good discrimination ability in the training cohort (area under the curve [AUC]: 0.845) and the validation cohort (AUC: 0.836). DCA demonstrated the most clinical benefits can be provided by the nomogram. Conclusions: Our developed preoperative nomogram model can successfully evaluate the neoplastic potential of GBPs based on simple clinical variables, that maybe useful for clinical decision-making.

Sign in / Sign up

Export Citation Format

Share Document