Glottal Area Segmentation without Initialization using Gabor Filters

Méndez, Amaia; Garcı́a, Begoña; Ruiz, I.; Iturricha, Ibai

doi:10.1109/isspit.2008.4775678

Cited by 14 publications

(9 citation statements)

References 14 publications

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“…Different glottal area segmentation approaches have been reported [17][18][19][20][21][22][23]. The glottal area is defined as the space in between the vocal folds.…”

Section: Segmentation Of the Glottis And Normalization Of The Vocal Fmentioning

confidence: 99%

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

Turkmen¹,

Karslıgil²,

Koçak³

2015

Computers in Biology and Medicine

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“…Different glottal area segmentation approaches have been reported [17][18][19][20][21][22][23]. The glottal area is defined as the space in between the vocal folds.…”

Section: Segmentation Of the Glottis And Normalization Of The Vocal Fmentioning

confidence: 99%

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

Turkmen¹,

Karslıgil²,

Koçak³

2015

Computers in Biology and Medicine

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“…A combination of the methods described in the previous section was used in both. Carrying out the segmentation of the glottal space with Gabor filters formed part of a previous research project [8][9].…”

Section: Segmentation Algorithms Designmentioning

confidence: 99%

“…First frame (F1) is segmented by Gabor Filtering method [8] The Chan-Vese algorithm block inputs are the result of previous block (Segmented F1) and the others frames of the sequence (F2 to FX). In this case, the initialisation curve C used by the Chan-Vese algorithm is the result of previously segmenting the area of interest using Gabor filters (Segmented F1).…”

Section: Algorithm 1 Descriptionmentioning

confidence: 99%

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Vocal folds paralysis study using a pre-processing stage of Gabor filtering and Chan-Vese segmentation

Zorrilla

Zapirain

2010

The 10th IEEE International Symposium on Signal Processing and Information Technology

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This paper describes the methodology proposed by the authors to diagnose vocal folds paralysis automatically using vocal folds stroboscopic images. Previously, a segmentation stage is necessary to obtain correctly and quickly the glottal space from healthy and pathological vocal folds video sequences captured by the laryngoscope. The designed segmentation method combines Gabor filtering and Chan-Vese algorithm. To evaluate the quality of the segmentation algorithms some parameters have been measured: the segmentation of the region of interest, the frames incorrectly segmented (in %) and the number of iterations used by the designed algorithm (in %). This study is specifically focused on measuring the opening angles in healthy and vocal folds with paralysis. This kind of parameters help the specialist to express a diagnosis based on something more robust than the observation of medical images. It shows a great advance in design, and in the nearby future, a complete method to diagnose vocal folds pathologies.

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“…Kondisi pita suara dapat mengalami gangguan fisiologis sementara atau permanen yang mengakibatkan perubahan suara yang dihasilkan, sehingga menyebabkan terganggunya aktivitas sehari-hari dalam berkomunikasi, bahkan mengakibatkan kematian [3]. Salah satu gold standard yang digunakan oleh dokter untuk melakukan diagnosis kondisi pita suara adalah dengan menggunakan alat laringoskop.…”

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Sistem Klasifikasi Kondisi Pita Suara dengan Metode Decision Tree

Bethaningtyas

Suwandi

Anggraini

2019

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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This paper presents the study of pathological vocal cords classification using digital image processing. There are six classifications of vocal cords, including the normal vocal cords condition. Before the classification process, image vocal cords are extracted to obtain the characteristics or information of objects in the image. In this study, shape measurement is used to extract the glottis contour of the vocal cords that can be analyzed and classified. The process of measuring the glottis contour of vocal cords requires the vocal image in the binary image. To get the binary image, this study uses a method to automatically obtain the glottis area segmentation without user initialization. The segmentation is mainly based on active contour, which is Chan-Vese algorithm. The result of this study can optimize glottis contour extraction and results of the classification training process using Decision Tree algorithm obtains an accuracy of 98.3%. Intisari-Makalah ini menyajikan studi tentang klasifikasi kondisi pita suara menggunakan pengolahan citra digital. Ada enam klasifikasi dari pita suara termasuk kondisi normal pita suara. Sebelum proses klasifikasi, gambar pita suara diekstraksi untuk mendapatkan karakteristik atau informasi objek dalam gambar. Dalam makalah ini digunakan pengukuran bentuk untuk mengekstrak kontur glotis pita suara yang dapat dianalisis dan diklasifikasikan. Proses mengukur kontur glotis pita suara membutuhkan citra biner pita suara. Untuk mendapatkan citra biner digunakan metode segmentasi otomatis tanpa inisialisasi pengguna. Segmentasi dilakukan menggunakan kontur aktif, yaitu algoritme Chan-Vese. Hasil studi ini dapat mengoptimalkan ekstraksi kontur glotis pita suara dan hasil dari proses pelatihan klasifikasi yang menggunakan algoritme pohon keputusan mendapatkan akurasi 98,3%.

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Glottal Area Segmentation without Initialization using Gabor Filters

Cited by 14 publications

References 14 publications

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

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

Vocal folds paralysis study using a pre-processing stage of Gabor filtering and Chan-Vese segmentation

Sistem Klasifikasi Kondisi Pita Suara dengan Metode Decision Tree

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