Salam, rekan Nawala! Semoga kalian selalu dalam keadaan sehat.
Ini adalah IAES Nawala dari Institute of Advanced Engineering and Science. Hari ini kami akan berbagi kabar tentang pemrosesan citra medis. Pemrosesan citra medis adalah studi tentang penggunaan berbagai teknik dan algoritma untuk menganalisis dan menginterpretasikan citra medis yang dihasilkan dari modalitas pencitraan seperti sinar-X, CT scan, MRI, USG, dan lainnya. Pemrosesan citra medis bertujuan untuk meningkatkan informasi dalam gambar, membantu diagnosis, dan memfasilitasi penelitian medis. Beberapa aspek penting dalam pemrosesan citra medis meliputi akuisisi citra, preprocessing citra, segmentasi citra, ekstraksi fitur, registrasi citra, fusi citra, deteksi dan diagnosis penyakit, diagnosis dengan bantuan komputer (CAD), visualisasi 3D, serta pembelajaran mesin dan pembelajaran mendalam.
Alqaisi dan George (2022) membahas deteksi dini dan klasifikasi melanoma dengan menggunakan jaringan saraf tiruan (JST) dan dataset ISIC 2018. Pentingnya deteksi kanker kulit ditekankan sebagai upaya mencegah perkembangan dan hasil yang potensial fatal. Dataset ISIC 2018 digunakan karena berisi gambar dermoskopi yang beragam, khususnya untuk empat jenis melanoma. Tahapan pra-pemrosesan melibatkan empat langkah penting, seperti penghilangan partikel rambut, pemangkasan, penipisan, dan normalisasi, untuk mengisolasi area kanker dalam gambar kulit. Transformasi domain frekuensi, seperti DCT, DWT, dan transformasi gradien, diterapkan untuk mengubah gambar menjadi koefisien domain frekuensi. Metode ekstraksi fitur statistik digunakan untuk memadatkan ukuran data agar pelatihan JST lebih efisien dan menangkap fitur-fitur penting dari gambar yang ditransformasikan. Terakhir, menggunakan JST untuk klasifikasi melanoma.
Asmaa Abdulrazaq Alqaisi, Loay Edwar George
Skin cancer is one of the most dangerous types of cancer. Some types of this cancer lead to death, so cancer must be discovered and indexed to avoid its spread through initial detection in the impulsive stage. This paper deals with the detection and indexing of different types of melanomas using an artificial neural network (ANN) depending on the international skin imaging collaboration (ISIC) 2018 dataset that was used. The pre-processing is the most important part because it formulates an image by insolated the cancer part from the skin image. It consists of four stages, removable, cropping, thinning, and normalization. This phase has been used to eliminate all the undesirable hair particles on the image lesion. The cropped image transforms into frequency domain coefficients using discrete cosine transform (DCT), discrete wavelet transform (DWT), and gradient transform for sub-band images to extract its feature. The statistical feature extraction is implemented to minimize the size of data for ANN training. The experimental analysis used dataset ISIC 2018 consisting of seven different types of dermoscopic images (this paper deals with four types only). For classification purposes, ANN was implemented and the accuracy obtained is about 88.98% for DWT, 85.44% for sub-band DCT, and 76.07% for sub-band gradient transform.
Di sisi lain Amusa dkk. (2022) membahas tentang kebutuhan akan metode peningkatan citra medis yang efisien dan serbaguna. Solusi yang diusulkan adalah teknik tri-modal yang menggabungkan masking tidak tajam, transformasi logaritmik, dan pendekatan penyetaraan histogram. Evaluasi dilakukan dengan menggunakan tiga jenis gambar medis dan metrik kinerja tertentu.
Tri-modal technique for medical images enhancement
Kamoli Akinwale Amusa, Olumayowa Ayodeji Idowu, Isaiah Adediji Adejumobi, Gboyega
Owing to methods of acquisition, medical images often require enhancement for them to serve the intended purpose of computer-aided diagnosis. Most medical image enhancement techniques are application specific, leading to the introduction of different enhancement methods for different medical images. In addition, the execution time of most of the previous enhancement methods is longer than necessary. Hence, there is a need for a method that produces fast and satisfactory results when deployed for the enhancement of several medical images. This paper proposes a tri-modal technique, involving a hybrid combination of unsharp masking, logarithmic transformation, and histogram equalization approaches, for medical image enhancement. Three classes of medical images: X-ray, magnetic resonance, and computer tomographic images are used for the evaluations of the proposed tri-modal method, where absolute mean brightness error, peak signal-to-noise ratio, and entropy are utilized as performance metrics. Both qualitative and quantitative evaluations reveal that the proposed tri-modal method performed better than the four previous methods in the literature for the three classes of medical images used in the evaluation. Also, the execution time of the tri-modal technique compares well with those of mono-mode methods. Thus, the tri-modal technique produces better enhanced medical images from different medical image inputs.
Beberapa artikel di atas merupakan bagian kecil dari penelitian mengenai pemrosesan citra medis. Untuk mendapatkan informasi lebih lanjut, pembaca dapat mengunjungi laman International Journal of Advances in Applied Sciences (IJAAS) dan membaca artikel secara GRATIS melalui tautan berikut: https://ijaas.iaescore.com/.
Redaksi: I. Busthomi
