H. Irem Turkmen

Classification of laryngeal disorders based on shape and vascular defects of vocal folds

Vocal fold disorders such as laryngitis, vocal nodules, and vocal polyps may cause hoarseness, breathing and swallowing difficulties due to vocal fold malfunction. Despite the fact that state of the art medical imaging techniques help physicians to obtain more detailed information, difficulty in differentiating minor anomalies of vocal folds encourages physicians to research new strategies and technologies to aid the diagnostic process. Recent studies on vocal fold disorders note the potential role of the vascular structure of vocal folds in differential diagnosis of anomalies. However, standards of clinical usage of the blood vessels have not been well established yet due to the lack of objective and comprehensive evaluation of the vascular structure. In this paper, we present a novel approach that categorizes vocal folds into healthy, nodule, polyp, sulcus vocalis, and laryngitis classes exploiting visible blood vessels on the superior surface of vocal folds and shapes of vocal fold edges by using image processing techniques and machine learning methods. We first detected the vocal folds on videolaryngostroboscopy images by using Histogram of Oriented Gradients (HOG) descriptors. Then we examined the shape of vocal fold edges in order to provide features such as size and splay portion of mass lesions. We developed a new vessel centerline extraction procedure that is specialized to the vascular structure of vocal folds. Extracted vessel centerlines were evaluated in order to get vascular features of vocal folds, such as the amount of vessels in the longitudinal and transverse form. During the last step, categorization of vocal folds was performed by a novel binary decision tree architecture, which evaluates features of the vocal fold edge shape and vascular structure. The performance of the proposed system was evaluated by using laryngeal images of 70 patients. Sensitivity of 86%, 94%, 80%, 73%, and 76% were obtained for healthy, polyp, nodule, laryngitis, and sulcus vocalis classes, respectively. These results indicate that visible vessels of vocal folds can act as a prognostic marker for vocal fold pathologies, as well as the vocal fold shape features, and may play a critical role in more effective diagnosis.

Assessment of videolaryngostroboscopy images based on visible vessels of vocal folds

Extraction of vessel structures and the vessel features automatically forms an essential step for computer-aided diagnosis. Visible vessels of vocal folds become a diagnostic aid for vocal fold pathologies by publication of limited number of researches which analyze the effects of vocal fold pathologies on visual characteristics of blood vessels. In this paper we present a novel system that extracts blood vessels centerlines on vocal folds images and detects pathologically altered vocal folds exploiting visual characteristics of vessels.

Classification of vocal fold nodules and cysts based on vascular defects of vocal folds

Physical examination of larynx by using videolaryngostroboscopy provides valuable information for diagnosis of vocal fold pathologies. However difficulty of differentiate nodules and cysts using clinical resources alone motivates physicians to research new strategies. In this paper, we propose a novel approach that performs nodule-cyst classification exploiting visible blood vessels on the superior surface of vocal folds. We first detected the region of vocal folds on videolaryngostroboscopy images and then extracted centerlines of vessel network on vocal folds. We used orientation pattern of vessels for classification. The performance of the proposed system was evaluated using laryngeal images of 21 patients. True positive rates of 76% and 74% were obtained for nodule and cyst classes respectively. These results indicate that visible vessels of vocal folds may play a critical role in more effective diagnosis of vocal fold pathologies like nodule and cyst which may be difficult to differentiate.

Vehicle identification using acoustic and seismic signals

In this study, we have designed a vehicle classification system which classifies Assault Amphibian Vehicle and Dragon Wagon, using acoustic and sesimic features. We implemented Mel Frequency Cepstral Coefficient (MFCC) algorithm to extract features of the acoustic and sesimic data, and these extracted features were reduced by using Vector Quantizaton algorithm. Both Support Vector Machine (SVM) and k-Nearest Neighborhood (k-NN) algorithms were implemented and their classification performances were evaluated.

Linear Discriminant Analysis in Ottoman Alphabet Character Recognition

This paper proposes a novel Linear Discriminant Analysis (LDA) based Ottoman Character Recognition system. Linear Discriminant Analysis reduces dimensionality of the data while retaining as much as possible of the variation present in the original dataset. In the proposed system, the training set consisted of 33 classes for each character of Ottoman language alphabet. First the training set images were normalized to reduce the variations in illumination and size. Then characteristic features were extracted by LDA. To apply LDA, the number of samples in train set must be larger than the features of each sample. To achieve this, Principal Component Analysis (PCA) were applied as an intermediate step. The described processes were also applied to the unknown test images. K-nearest neighborhood approach was used for classification.

Visible Vessels Of Vocal Folds: Can They Have A Diagnostic Role?

BACKGROUND Challenges in visual identification of laryngeal disorders lead researchers to investigate new opportunities to help clinical examination. This paper presents an efficient and simple method which extracts and assesses blood vessels on vocal fold tissue in order to serve medical diagnosis. METHODS The proposed vessel segmentation approach has been designed in order to overcome difficulties raised by design specifications of videolaryngostroboscopy and anatomic structure of vocal fold vasculature. The limited number of medical studies on vocal fold vasculature point out that the direction of blood vessels and amount of vasculature are discriminative features for vocal fold disorders. Therefore, we extracted the features of vessels on the basis of these studies. We represent vessels as vascular vectors and suggest a vector field based measurement that quantifies the orientation pattern of blood vessels towards vocal fold pathologies. RESULTS In order to demonstrate the relationship between vessel structure and vocal fold disorders, we performed classification of vocal fold disorders by using only vessel features. A binary tree of Support Vector Machine (SVM) has been exploited for classification. Average recall of proposed vessel extraction method was calculated as 0.82 while healthy, sulcus vocalis, laryngitis classification accuracy of 0.75 was achieved. CONCLUSION Obtained success rates showed the efficiency of vocal fold vessels in serving as an indicator of laryngeal diseases.

Superpixel-based segmentation of glottal area from videolaryngoscopy images

Abstract. Segmentation of the glottal area with high accuracy is one of the major challenges for the development of systems for computer-aided diagnosis of vocal-fold disorders. We propose a hybrid model combining conventional methods with a superpixel-based segmentation approach. We first employed a superpixel algorithm to reveal the glottal area by eliminating the local variances of pixels caused by bleedings, blood vessels, and light reflections from mucosa. Then, the glottal area was detected by exploiting a seeded region-growing algorithm in a fully automatic manner. The experiments were conducted on videolaryngoscopy images obtained from both patients having pathologic vocal folds as well as healthy subjects. Finally, the proposed hybrid approach was compared with conventional region-growing and active-contour model-based glottal area segmentation algorithms. The performance of the proposed method was evaluated in terms of segmentation accuracy and elapsed time. The F-measure, true negative rate, and dice coefficients of the hybrid method were calculated as 82%, 93%, and 82%, respectively, which are superior to the state-of-art glottal-area segmentation methods. The proposed hybrid model achieved high success rates and robustness, making it suitable for developing a computer-aided diagnosis system that can be used in clinical routines.

Detection of visible vessels of vocal folds in videolaryngostroboscopy images

The clinical diagnosis of voice pathologies is based on examination of structural defect of the vocal folds with video laryngostroboscope. Clinical studies show that pathologies not only cause vocal fold mulfunction but also effect the visual characteristics of blood vessels on the superior surface of vocal folds. In this article, we propose a novel system that extracts the vocal folds vessels in the images acquired during routine videolaryngostroboscopy and help physicians in presenting the relationship between blood vessel defect and vocal fold pathologies.

Demographic information classification exploiting spoken language

Recently, extracting the demographic information like age, gender and race by using speech and face attributes takes much attention in the literature. In this research, we have focused on the implementation of a demographic information classification system and proved the relationship between spoken language and demographic profile of people. In the first step, the feature vectors of spoken language were extracted then dimensions of the feature vectors were reduced by our feature reduction method and Correlation Based Feature Selection method. Finally, the success of Naïve Bayes, Support Vector Machine and K-Nearest Neigbour classification algorithms was evaluated.

Reconstruction of dysphonic speech for synthesizing normally phonated speech

In this study, a novel system, delivering synthetic speech with the quality near to natural, is designed and implemented by reconstructing dysphonic speech of patients that have lost their voice totally due to apoplectic chordae vocalis, organic lesions of vocal cords or partial laryngectomy.