scholarly journals Pixel Classification Methods to Detect Skin Lesions on Dermoscopic Medical Images

2017 ◽  
pp. 444-455 ◽  
Author(s):  
Fabrizio Balducci ◽  
Costantino Grana
Keyword(s):  
Medical Images ◽  
Skin Lesions ◽  
Classification Methods ◽  
Pixel Classification

Classification of Medical Images in the Domain of Melanoid Skin Lesions

2008 ◽  
pp. 519-526 ◽  
Author(s):  
Zdzislaw S. Hippe ◽  
Jerzy Grzymala-Busse ◽  
Piotr Blajdo ◽  
Maksymilian Knap ◽  
Teresa Mroczek ◽  
...  
Keyword(s):  
Medical Images ◽  
Skin Lesions

scholarly journals Thyroid Nodule Recognition Based on Feature Selection and Pixel Classification Methods

2012 ◽  
Vol 26 (1) ◽  
pp. 119-128 ◽  
Author(s):  
Dorin Bibicu ◽  
Luminita Moraru ◽  
Anjan Biswas
Keyword(s):  
Feature Selection ◽  
Thyroid Nodule ◽  
Classification Methods ◽  
Pixel Classification

Classification and synthesis of medical images in the domain of melanocytic skin lesions

2008 ◽  
Author(s):  
Teresa Mroczek ◽  
Wieslaw Paja ◽  
Lukasz Piatek ◽  
Mariusz Wrzesie
Keyword(s):  
Medical Images ◽  
Skin Lesions

scholarly journals Pixel classification methods for identifying and quantifying leaf surface injury from digital images

2014 ◽  
Vol 108 ◽  
pp. 155-165 ◽  
Author(s):  
Ole Mathis Opstad Kruse ◽  
José Manuel Prats-Montalbán ◽  
Ulf Geir Indahl ◽  
Knut Kvaal ◽  
Alberto Ferrer ◽  
...  
Keyword(s):  
Leaf Surface ◽  
Digital Images ◽  
Classification Methods ◽  
Pixel Classification

scholarly journals Pengaruh Tingkat Kompresi Citra ALOS AVNIR-2 terhadap Akurasi Hasil Transformasi Indeks Vegetasi dan Klasifikasi Penutup Lahan Wilayah Salatiga dan Ambarawa, Jawa Tengah

2020 ◽  
Vol 34 (2) ◽  
pp. 130
Author(s):  
Projo Danoedoro
Keyword(s):  
Image Compression ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Original Data ◽  
Data Availability ◽  
Classification Methods ◽  
Quality Of Data ◽  
Pixel Classification ◽  
Central Java ◽  
Object Based

Abstrak Penggunaan teknik kompresi untuk menghemat ukuran penyimpanan citra digital telah banyak dijumpai dalam aplikasi keseharian. Di sisi lain, kompresi citra juga dapat memberikan konsekuensi berupa kehilangan detil data, yang akan berpengaruh pada integritas data. dan secara teoretis juga akan berpengaruh pada kualitas turunan data.  Penelitian ini mengkaji pengaruh tingkat kompresi citra digital multispektral ALOS-AVNIR2 yang terdiri dari empat saluran dengan resolusi spasial 10 meter terhadap akurasi hasil transformasi indeks vegetasi dan  klasifikasi penutup lahan untuk wilayah Salatiga-Ambarawa, Jawa Tengah.  Citra dikompresi pada sembilan tingkat, yaitu dari tidak kehilangan detil sama sekali (100%, atau sama dengan data asli) hingga 10%, dengan interval 10%. Indeks Vegetasi yang diterapkan meliputi NDVI, TVI dan MSARVI. Klasifikasi multispektral yang diujicobakan meliputi  klasifikasi per-piksel  dan klasifikasi berbasis objek.  Hasil penelitian ini menunjukkan bahwa transformasi indeks vegetasi dan klasifikasi per-piksel mengalami penurunan akurasi secara drastis, sejalan dengan meningkatnya kompresi citra, sementara klasifikasi berbasis objek mengalami perubahan akurasi relatif lebih sedikit dibandingkan analisis per-piksel. Temuan penelitian ini menunjukkan bahwa penggunaan citra terkompresi sebagai masukan proses klasifikasi secara digital sebaiknya dihindari. Meskipun demikian, kalau pun terpaksa dilakukan karena masalah ketersediaan data, maka metode klasifikasi berbasis objeklah yang sebaiknya diterapkan; dan untuk klasifikasi per-piksel maka algoritma jarak minimum terhadap rerata-lah yang  sebaiknya dipilih. Abstract The use of compression techniques for saving storage space of digital imagery has been commonly found in daily applications.  On the other hand, image compression can also provide consequences of losing data details, which will affect data integrity and theoretically will also affect the quality of data derived. This study examined the effect of ALOS-AVNIR2 multispectal image compression level consisting of four channels with 10 m spatial resolution to the accuracies of vegetation index transformation and land cover classification for Salatiga and Ambarawa region, Central Java. This study compressed the image into nine levels, i.e. from lossless details (100%, or equal to original data) up to 10% compression, at 10% intervals. The applied vegetation indices include NDVI, TVI and MSARVI. The multispectral classifications that were piloted include the per-pixel and object-based classification methods. The results of this study indicated that the vegetation index transformation and per-pixel classification have drastically decreased accuracies, in line with the increase in image compression; while the object-based classification has relatively more stable than per-pixel analysis. The findings of this study showed that the use of compressed imagery as an input to digital classification process should be avoided. However, even if it has to be done due to data availability issues, then object-based classification methods should be applied; and especially for per-pixel classification,  the minimum distance to mean algorithm should be chose.

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