Brain Tumor Segmentation using SLIC Superpixels and Optimized Thresholding Algorithm

Adjei, Prince Ebenezer; Nunoo‐Mensah, Henry; Agbesi, Richard J. Amedzrovi; Ndjanzoue, Joyce Raissa Yaho

doi:10.5120/ijca2018917915

Cited by 6 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Selain itu, penelitian yang dilakukan oleh [16] mengenai klasifikasi penutup lahan fusi citra lidar dan citra optik udara dengan menerapkan Superpixel Colorization menghasilkan rata-rata akurasi sebesar 90,40%. Penelitian lain juga banyak menggunakan segmentasi superpixel untuk pengolahan citra [17], [18], [19], [20].…”

Section: Pendahuluanunclassified

Klasifikasi Video Olahraga Berdasarkan Citra Berbasis Konten Menggunakan Segmentasi Superpixel

Yohannes¹,

Rachmat²,

Saputra³

2022

Komputika

View full text Add to dashboard Cite

Olahraga memiliki jumlah yang banyak dan semakin bertambah dari waktu ke waktu. Banyak orang yang suka menyaksikan perlombaan atau turnamen olahraga di lapangan secara langsung. Berkat kemajuan teknologi, kini turnamen olahraga bisa disaksikan secara digital melalui video tanpa perlu menontonnya secara langsung di lapangan. Klasifikasi video perlu dilakukan untuk membedakan banyaknya video olahraga saat ini ke dalam kategori olahraga yang sesuai dengan kontennya. Penelitian ini mengklasifikasikan video olahraga berjenis Baseball, Basketball, Boxing, Gymnastic, Hockey, Swimming, Tennis, Volleyball, dan Wrestling berdasarkan citra berbasis konten. Klasifikasi dilakukan dengan konten citra sebagai fitur, kemudian dilakukan proses segmentasi superpixel dan superpixel colorization pada citra untuk membentuk model. Model dibentuk menggunakan metode CNN dengan arsitektur ResNet-50. Berbagai jumlah segmentasi superpixel digunakan sebagai perbandingan dalam pembentukan model untuk mendapatkan hasil yang terbaik yaitu 25.000 dengan tingkat rata-rata accuracy 0,97; precision 0,87; dan recall 0,86 sehingga digunakan untuk pengujian video olahraga. Hasil pengujiannya yaitu segmentasi superpixel bisa digunakan untuk mengklasifikasikan video olahraga dengan baik dengan tingkat rata-rata accuracy 0,91; precision 0,64; dan recall 0,61, walaupun terdapat beberapa olahraga yang memiliki hasil yang kurang baik seperti basketball dan wrestling.

show abstract

Section: Pendahuluanunclassified

Klasifikasi Video Olahraga Berdasarkan Citra Berbasis Konten Menggunakan Segmentasi Superpixel

Yohannes¹,

Rachmat²,

Saputra³

2022

Komputika

View full text Add to dashboard Cite

show abstract

“…Image segmentation techniques in computer vision partition the image into several parts based on specific image features like pixel intensity value, color, and texture [1]. Numerous approaches for brain image segmentation [2] have been proposed. Around these approaches work on the values of the clustering model and intensity threshold procedures.…”

Section: Introductionmentioning

confidence: 99%

A hybrid method for traumatic brain injury lesion segmentation

Dawod

Phaphuangwittayakul

Angkurawaranon

2022

IJECE

View full text Add to dashboard Cite

<span>Traumatic brain injuries are significant effects of disability and loss of life. Physicians employ computed tomography (CT) images to observe the trauma and measure its severity for diagnosis and treatment. Due to the overlap of hemorrhage and normal brain tissues, segmentation methods sometimes lead to false results. The study is more challenging to unitize the AI field to collect brain hemorrhage by involving patient datasets employing CT scans images. We propose a novel technique free-form object model for brain injury CT image segmentation based on superpixel image processing that uses CT to analyzing brain injuries, quite challenging to create a high outstanding simple linear iterative clustering (SLIC) method. The maintains a strategic distance of the segmentation image to reduced intensity boundaries. The segmentation image contains marked red hemorrhage to modify the free-form object model. The contour labelled by the red mark is the output from our free-form object model. We proposed a hybrid image segmentation approach based on the combined edge detection and dilation technique features. The approach diminishes computational costs, and the show accomplished 96.68% accuracy. The segmenting brain hemorrhage images are achieved in the clustered region to construct a free-form object model. The study also presents further directions on future research in this domain.</span>

show abstract

SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging

et al. 2020

View full text Add to dashboard Cite

Brain Tumor Segmentation using SLIC Superpixels and Optimized Thresholding Algorithm

Cited by 6 publications

References 15 publications

Klasifikasi Video Olahraga Berdasarkan Citra Berbasis Konten Menggunakan Segmentasi Superpixel

Klasifikasi Video Olahraga Berdasarkan Citra Berbasis Konten Menggunakan Segmentasi Superpixel

A hybrid method for traumatic brain injury lesion segmentation

SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging

Contact Info

Product

Resources

About