Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

<div>Accuracies of the segmentation with different <svg height="8.8423pt" id="M137" style="vertical-align:-0.2064009pt" version="1.1" viewbox="-0.0498162 -8.6359 11.0475 8.8423" width="11.0475pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M822 650H589L583 622C660 617 677 607 674 561C672 534 664 481 647 390L600 137H596L273 650H126L120 622C176 620 194 615 207 594C221 571 225 557 214 504L161 257C141 166 129 112 121 85C108 42 83 30 29 28L23 0H260L266 28C193 33 173 42 176 89C178 122 186 172 202 255L256 527H259L583 -8H612L690 390C708 481 720 535 728 558C744 603 756 619 816 622L822 650Z" id="g113-79"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="839" vert-adv-y="839"></glyph.data></g></svg> and <svg height="11.9316pt" id="M138" style="vertical-align:-3.2957pt" version="1.1" viewbox="-0.0498162 -8.6359 34.4552 11.9316" width="34.4552pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M822 650H589L583 622C660 617 677 607 674 561C672 534 664 481 647 390L600 137H596L273 650H126L120 622C176 620 194 615 207 594C221 571 225 557 214 504L161 257C141 166 129 112 121 85C108 42 83 30 29 28L23 0H260L266 28C193 33 173 42 176 89C178 122 186 172 202 255L256 527H259L583 -8H612L690 390C708 481 720 535 728 558C744 603 756 619 816 622L822 650Z" id="g113-79"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="839" vert-adv-y="839"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,10.062,3.132)"><path d="M327 325C324 350 312 408 304 433C283 442 256 451 213 451C120 451 59 391 59 320C59 243 125 210 188 181C237 158 266 136 266 93C266 52 231 24 195 24C134 24 93 81 74 148L42 143C42 106 51 37 59 23C78 8 125 -12 177 -12C257 -12 345 35 345 127C345 198 292 234 216 264C171 283 131 305 131 348C131 387 156 416 195 416C243 416 277 378 297 317L327 325Z" id="g58-113"></path><glyph.data ascent="1007" descent="-369" horiz-adv-x="376" vert-adv-y="376"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,13.557,3.132)"><path d="M384 109C349 76 310 59 268 59C197 59 123 113 120 245C240 248 367 259 384 262C403 266 407 275 407 296C407 376 338 451 254 451H253C201 451 148 425 105 378C65 334 39 272 39 203C39 88 112 -12 235 -12C266 -12 339 5 402 84L384 109ZM230 411C285 411 319 365 319 313C319 298 313 293 294 293C236 291 180 290 125 290C141 370 185 411 230 411Z" id="g58-99"></path><glyph.data ascent="1007" descent="-369" horiz-adv-x="434" vert-adv-y="434"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,17.543,3.132)"><path d="M442 39L431 68C420 63 406 57 394 57C377 57 359 71 359 118V301C359 353 349 393 314 424C291 443 261 451 230 451C171 438 107 401 79 381C59 366 46 355 46 340C46 316 72 295 91 295C106 295 117 303 122 319C131 350 139 371 152 384C163 397 178 404 197 404C246 404 281 365 281 293V278C260 262 186 232 124 213C69 194 41 161 41 112C41 47 92 -12 163 -12C195 -12 243 25 283 51C288 35 293 22 308 7C319 -4 339 -12 354 -12L442 39ZM281 87C262 68 226 51 199 51C169 51 129 74 129 126C129 162 151 181 190 198C211 208 259 229 281 240V87Z" id="g58-95"></path><glyph.data ascent="1007" descent="-369" horiz-adv-x="449" vert-adv-y="449"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,21.546,3.132)"><path d="M185 344V453C137 433 92 421 41 413V386C100 378 104 375 104 310V106C104 41 97 35 33 30V0H268V30C190 35 185 41 185 107V283C206 340 240 371 267 371C282 371 296 364 310 351C316 344 325 343 336 348C357 357 369 378 369 399C369 424 345 451 310 451C262 451 219 397 187 344H185Z" id="g58-112"></path><glyph.data ascent="1007" descent="-369" horiz-adv-x="380" vert-adv-y="380"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,24.895,3.132)"><path d="M396 114C349 71 317 59 274 59C216 59 123 104 123 241C123 342 180 400 246 400C283 400 317 386 348 359C355 352 361 348 366 348C378 348 401 371 402 394C402 405 398 413 385 424C369 437 337 451 294 451H293C254 451 193 434 141 394C73 342 39 277 39 198C39 90 114 -12 243 -12C303 -12 371 31 415 90L396 114Z" id="g58-97"></path><glyph.data ascent="1007" descent="-369" horiz-adv-x="432" vert-adv-y="432"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,28.78,3.132)"><path d="M522 0V30C462 35 455 41 455 106V280C455 397 397 451 314 451C262 451 206 414 171 379V710C131 698 69 682 20 675V647C87 642 90 637 90 574V106C90 41 80 35 20 30V0H236V30C176 35 171 43 171 106V339C198 371 237 391 276 391C341 391 374 350 374 269V106C374 43 367 35 308 30V0H522Z" id="g58-102"></path><glyph.data ascent="1007" descent="-369" horiz-adv-x="544" vert-adv-y="544"></glyph.data></g></svg>.</div>

BioMed Research International

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Figure 7

Figure 7: Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images 