A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation

Zhang, Rui; Zhu, Shiping; Zhou, Qin

doi:10.3390/s16101756

Cited by 17 publications

(12 citation statements)

References 29 publications

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“…Perhitungan NBGGVFS Model GGVFS memperbesar jangkauan konvergensi kontur aktif, meningkatkan kinerja konvergensi dan lebih handal terhadap noise. NBGVF memberikan solusi untuk masalah perlindungan edge yang tipis [10]. Oleh karena itu, metode ini menggabungkan GGVF dan NBGVF untuk mengusulkan versi energi eksternal yang baru, yang didefinisikan sebagai berikut:…”

Section: IIIunclassified

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Segmentasi Citra Sel Tunggal Smear Serviks Menggunakan Metode Radiating Normally Biased Generalized Gradient Vector Flow Snake

Susanti

Tjandrasa

Fatichah

2018

JUTI

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Proses segmentasi nukleus dan sitoplasma dibutuhkan dalam sistem diagnosa otomatis pemeriksaan kanker serviks karena dapat mempengaruhi keakuratan sistem. Sebuah algoritma energi eksternal untuk active contour (snake) dengan menggabungkan metode Normally Biased Gradient Vector Flow Snake dan Generalized Gradient Vector Flow Snake diusulkan pada penelitian ini. GGVF dapat memperbesar jangkauan konvergensi kontur aktif, meningkatkan kinerja konvergensi dan lebih handal terhadap noise, serta NBGVF dapat memberikan solusi untuk masalah perlindungan tepi yang tipis. Oleh karena itu, penelitian ini menggabungkan GGVF dan NBGVF untuk mengusulkan versi energi eksternal yang baru yang disebut Normally Biased Generalized Gradient Vector Flow Snake (NBGGVFS). Radiating Edge Map dimanfaatkan sebagai fungsi edge map snake pada penelitian ini. Penelitian ini bertujuan untuk segmentasi sitoplasma dan nukleus citra sel tunggal smear serviks menggunakan metode Radiating Normally Biased Generalized Gradient Vector Flow Snake. Metode usulan terdiri atas tiga proses utama, yaitu pra proses, proses segmentasi awal dan segmentasi akhir. Metode segmentasi awal yang digunakan pada penelitian ini adalah mean shift clustering. Dataset yang digunakan adalah dataset Herlev. Proses uji coba dilakukan dengan cara memvariasikan nilai bandwidth metode segmentasi awal dan dengan membandingkan nilai akurasi dan ZSI metode usulan dengan metode penelitian sebelumnya. Berdasarkan uji coba, nilai rata-rata Zijdenbos Similarity Index (ZSI) dan akurasi untuk segmentasi nukleus adalah 96,96% dan 90,68%, dan untuk sitoplasma adalah 86,78% dan 89,35%. Hasil pengujian menunjukkan metode yang diusulkan mampu mendeteksi area nukleus dan sitoplasma pada citra sel tunggal smear serviks lebih optimal dibandingkan metode sebelumnya sehingga dapat digunakan sebagai metode segmentasi pada sistem identifikasi kanker serviks.

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Section: IIIunclassified

“…Deformasi Snake Active contour atau snake dapat dihitung dengan meminimasi fungsi energi eksternal dan internal yang dihitung dari data citra. Energi eksternal snake telah dihitung dengan menggunakan metode NBGGVFS [10]. Gambar Pada penelitian ini tidak semua citra pada dataset berhasil dianalisis.…”

Section: IIIunclassified

Segmentasi Citra Sel Tunggal Smear Serviks Menggunakan Metode Radiating Normally Biased Generalized Gradient Vector Flow Snake

Susanti

Tjandrasa

Fatichah

2018

JUTI

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“…Massive algorithms have been proposed for image segmentation [ 34 , 35 , 36 , 37 , 38 ], while few methods are explored to fulfill the necessities of UST image segmentation in terms of accuracy and efficiency. Both the K-means [ 28 ] and the thresholding [ 29 ] methods are relatively straightforward and only pixel intensities are taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

“…Watershed methods classify pixels into regions using gradient descent and analyzes weak points along region boundaries [ 39 , 40 ]. SNAKE (also known as active contour) utilizes splines to fit image structures of lines and edges [ 26 , 32 , 37 ]. Watershed and SNAKE methods benefit from the distinguishable intensity difference between the breast boundary and its surrounding regions.…”

Section: Introductionmentioning

confidence: 99%

Efficient Segmentation of a Breast in B-Mode Ultrasound Tomography Using Three-Dimensional GrabCut (GC3D)

Zhuang

et al. 2017

Sensors

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As an emerging modality for whole breast imaging, ultrasound tomography (UST), has been adopted for diagnostic purposes. Efficient segmentation of an entire breast in UST images plays an important role in quantitative tissue analysis and cancer diagnosis, while major existing methods suffer from considerable time consumption and intensive user interaction. This paper explores three-dimensional GrabCut (GC3D) for breast isolation in thirty reflection (B-mode) UST volumetric images. The algorithm can be conveniently initialized by localizing points to form a polygon, which covers the potential breast region. Moreover, two other variations of GrabCut and an active contour method were compared. Algorithm performance was evaluated from volume overlap ratios (TO, target overlap; MO, mean overlap; FP, false positive; FN, false negative) and time consumption. Experimental results indicate that GC3D considerably reduced the work load and achieved good performance (TO = 0.84; MO = 0.91; FP = 0.006; FN = 0.16) within an average of 1.2 min per volume. Furthermore, GC3D is not only user friendly, but also robust to various inputs, suggesting its great potential to facilitate clinical applications during whole-breast UST imaging. In the near future, the implemented GC3D can be easily automated to tackle B-mode UST volumetric images acquired from the updated imaging system.

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“…Therefore, infrared target detection, recognition and tracking are important topics in infrared image processing, in which the infrared image segmentation is one of the fundamental steps. In the computer vision and image processing fields, various methods have been proposed to solve the image segmentation problems [ 1 , 2 , 3 ]. However, due to the particular properties of infrared images, infrared image segmentation is still a challenging problem.…”

Section: Introductionmentioning

confidence: 99%

Thermal Infrared Pedestrian Image Segmentation Using Level Set Method

Qiao

Wei

Zhao

2017

Sensors

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The edge-based active contour model has been one of the most influential models in image segmentation, in which the level set method is usually used to minimize the active contour energy function and then find the desired contour. However, for infrared thermal pedestrian images, the traditional level set-based method that utilizes the gradient information as edge indicator function fails to provide the satisfactory boundary of the target. That is due to the poorly defined boundaries and the intensity inhomogeneity. Therefore, we propose a novel level set-based thermal infrared image segmentation method that is able to deal with the above problems. Specifically, we firstly explore the one-bit transform convolution kernel and define a soft mark, from which the target boundary is enhanced. Then we propose a weight function to adaptively adjust the intensity of the infrared image so as to reduce the intensity inhomogeneity. In the level set formulation, those processes can adaptively adjust the edge indicator function, from which the evolving curve will stop at the target boundary. We conduct the experiments on benchmark infrared pedestrian images and compare our introduced method with the state-of-the-art approaches to demonstrate the excellent performance of the proposed method.

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A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation

Cited by 17 publications

References 29 publications

Segmentasi Citra Sel Tunggal Smear Serviks Menggunakan Metode Radiating Normally Biased Generalized Gradient Vector Flow Snake

Segmentasi Citra Sel Tunggal Smear Serviks Menggunakan Metode Radiating Normally Biased Generalized Gradient Vector Flow Snake

Efficient Segmentation of a Breast in B-Mode Ultrasound Tomography Using Three-Dimensional GrabCut (GC3D)

Thermal Infrared Pedestrian Image Segmentation Using Level Set Method

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