Biomedical Image Processing / Medical Image Processing
Maryam Ashoori; Reza Aghaizadeh Zoroofi; Mohammad Sadeghi
Volume 17, Issue 2 , September 2023, , Pages 130-140
Abstract
Currently, the rapid growth of the beauty industry, along with the development of intelligent models based on machine learning algorithms, has led to an increase in extensive research in this field. Rhinoplasty is one of the most common and demanding facial cosmetic surgeries because the nose is the ...
Read More
Currently, the rapid growth of the beauty industry, along with the development of intelligent models based on machine learning algorithms, has led to an increase in extensive research in this field. Rhinoplasty is one of the most common and demanding facial cosmetic surgeries because the nose is the most prominence element of the face, which has a great impact on its attractiveness. The purpose of this article is to present a machine learning-based framework for predicting nasal aesthetic evaluation. In this article, a set of geometric parameters of the nose relative to the entire face are given as input and human rating as output to the popular machine learning regression algorithms. An ablation study was then carried out to examine the influence of facial shape, skin color, and texture on the beauty of the nose. Multilayer perceptron classification, K-means clustering, and grey level co-occurrence matrix were used to extract facial shape, skin color, and texture. The results show that the model based on geometric parameters has a moderate correlation with human rating, and by adding each subset of the features of face shape, color, and skin texture, the correlation of the obtained model increases until a high degree of correlation is achieved. The results also show that the random forest algorithm has the best performance among other algorithms based on the evaluation criteria of absolute mean error, root mean square error, and Pearson correlation. The results of this study show that the proposed framework can be helpful in determining the beauty of the nose.
Malihe Molaie; Reza Aghaeizadeh Zoroofi
Volume 13, Issue 3 , October 2019, , Pages 259-271
Abstract
Quantifying and modeling of the skeletal muscles can lead to an easier investigation of muscle diseases, specific mobility problems, and required simulations for the relevant surgeries. To this end, medical images should be segmented, firstly. In this research, thigh muscles segmentation is performed ...
Read More
Quantifying and modeling of the skeletal muscles can lead to an easier investigation of muscle diseases, specific mobility problems, and required simulations for the relevant surgeries. To this end, medical images should be segmented, firstly. In this research, thigh muscles segmentation is performed in CT images, since these muscles play a critical role in walking and balancing the body. To this aim, a multi-atlas method is used which is an improvement of the hierarchical multi-atlas method in the previous work. In this method, the muscles region is extracted automatically from the other tissues using FRFCM (Fast and Robust Fuzzy C-Means Clustering) method after the preprocessing stage. This muscle binary mask and the improved mask are used in the multi-atlas method for individual muscle segmentation. The proposed method is implemented using 20 CT data sets consisting of 12 female and 8 male subjects. The results show a less consumed computational time than the hierarchical multi-atlas method. The average computational time required for the muscles segmentation using the proposed method is 24 seconds and for the hierarchical multi-atlas method is 71 seconds per one slice of each case. Therefore, the proposed method reduces the implementation time by a rough factor of three. The means of the Dice similarity coefficient for the proposed method with improved muscle mask and for the hierarchical multi-atlas method are 86.58±7.69 and 83.07±8.26, respectively. The means of the precision and sensitivity for our method are 89.78±9.6 and 84.63±9.25, and for the hierarchical multi-atlas method are 88.85±12.04 and 78.04±10.88. Consequently, this method has better results based on the Dice similarity coefficient, precision, and sensitivity metrics.
Biomedical Image Processing / Medical Image Processing
Abbas Biniaz; Fatemeh Abdolali; Reza Aghaeizadeh Zoroofi; Omid Haji Maghsoudi; Yoshinobu Sato
Volume 12, Issue 4 , January 2019, , Pages 317-329
Abstract
Wireless capsule endoscopy is a non-invasive diagnosis method which allows recording a video as the capsule travels through the gastrointestinal tract. The practical drawback is producing a long clinical video up to 8 hours and it takes about 2 hours to review the exam by an experienced expert. Video ...
Read More
Wireless capsule endoscopy is a non-invasive diagnosis method which allows recording a video as the capsule travels through the gastrointestinal tract. The practical drawback is producing a long clinical video up to 8 hours and it takes about 2 hours to review the exam by an experienced expert. Video summarization methods can reduce the time required by experts and errors in manual interpretation. This paper presents an automatic method based on unique properties of adaptive singular value decomposition through sliding window that can reduce the long annotation time. By utilizing these properties, we are able to summarize a WCE video by outputting a motion video summary. Moreover, we apply an effective approach based on adaptive contrast diffusion to correct uneven illumination that deal with the low contrast generally caused by poor visibility conditions of the GI tract, WCE power and its structure. Experimental results on WCE videos indicate that a significant reduction in the review time is feasible. Quantitative and qualitative results of summarization show the effectiveness of proposed method that can be adapted to various clinical applications, such as training of young physicians, computer assisted diagnosis, medical decision support or medical document management.
Biomedical Image Processing / Medical Image Processing
Poune Roshani Tabrizi; Reza Aghaeizade Zoroofi
Volume 2, Issue 3 , June 2008, , Pages 247-266
Abstract
Drowsiness detection is vital in preventing traffic accidents. In this project, we propose three new algorithms for pupil and iris detection, lips localization and eyes state analysis, which we incorporate into a four step system for drowsiness detection: face detection, drowsiness parameters extraction ...
Read More
Drowsiness detection is vital in preventing traffic accidents. In this project, we propose three new algorithms for pupil and iris detection, lips localization and eyes state analysis, which we incorporate into a four step system for drowsiness detection: face detection, drowsiness parameters extraction from eyes, drowsiness parameter extraction from mouth and drowsiness level determination. Many current efforts, which are based on face analysis, focus only on using a single visual cue to characterize driver's state of alertness. This approach that relies on a single visual cue may encounter difficulty when the required visual features cannot be acquired accurately or reliably. There are few systems that use several visual cues to characterize driver's state of alertness. These systems are based on IR illuminators or training data. IR illuminators can be hazardous to eye health. Thus, our proposed system determines drowsiness level using a combination of several visual cues and contextual information. Also, it requires no training data at any step or IR illuminators. We analyzed and compared different parts of the systems with other methods using IMM, HCE, CVL database and 30 video sequences in two drowsy and active states from 15 persons. Finally, we achieved excellent drowsiness level results from the study population. We determined drowsiness level as follows: 1. The eyes and mouth state (detecting whether they were open or closed) was analyzed as 94.3% and 95.1 %, respectively; 2. Drowsiness level was determined in different situations such as normal blinking, fast blinking, normal speaking, yawning and long eye closure and 3. The participants were given a warning message when the drowsiness level reached over the threshold of 0.95.
Biomedical Image Processing / Medical Image Processing
Nader Riahi Alam; Reza Aghaeizade Zoroofi; Masoume Giti; Arian Deldari; Alireza Ahmadian
Volume 1, Issue 3 , June 2007, , Pages 157-165
Abstract
In this study, the need of a CAD system and its capabilities has been investigated and then a sample program for a mammographic CAD system proper to Iranian tropical patients was designed. In the first step, the analog mammographic images were digitized by 56 and 112 mm spatial resolution and then were ...
Read More
In this study, the need of a CAD system and its capabilities has been investigated and then a sample program for a mammographic CAD system proper to Iranian tropical patients was designed. In the first step, the analog mammographic images were digitized by 56 and 112 mm spatial resolution and then were processed by the designed sample program. Analysis and technical details for designing and implementing the program included for following steps: The capability of the program image displayer consisting of viewing four mammographic images from four breast views (RCC, RMLO, LCC, LMLO) in one window, determining breast region by background removing and other conventional preprocessing application tools; Software processing tools including theresholding, histogram, ROI determination; Patient information fields such as clinical information, conventional reporting section as used in radiological department in Iran; Computer-aided diagnostic section including proper diagnostic processing algorithm to automatic detection of breast abnormality. For instance the application of wavelet and fuzzy logic for detecting malignant clusters of microcalcification. The introduced mammographic CAD system can provide the collection, organizing and the availability of the patient local information. Therefore by using the prepared database the evaluation of the sensitivity and specifity of the detecting algorithm for comparison of different research methods would be possible.
